Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system

Computers & Industrial Engineering 63 (2012) 943–956

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Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system q R.J. Kuo a,⇑, W.L. Tseng d, F.C. Tien b, T. Warren Liao c a

Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Kee-Lung Road, Taipei, Taiwan, ROC Department of Industrial Engineering and Management, National Taipei University of Technology, No. 1, Section 3, Chung-Hsiao East Road, Taipei, Taiwan, ROC c Department of Construction Management and Industrial Engineering, Louisiana State University, 3128 CEBA, Baton Rouge, LA 70803, USA d Ting Hsin International Group, No.425, Wei-Ken Street, HEDA, Hang-Zhou, China b

a r t i c l e

i n f o

Article history: Received 6 July 2011 Received in revised form 21 January 2012 Accepted 19 June 2012 Available online 28 June 2012 Keywords: Radio frequency identification (RFID) Artificial immune system (AIS) Genetic algorithms (GAs) Fuzzy neural network (FNN)

a b s t r a c t Due to the rapid development of globalization, which makes supply chain management more complicated, more companies are applying radio frequency identification (RFID), in warehouse management. The obvious advantages of RFID are its ability to scan at high-speed, its penetration and memory. In addition to recycling, use of a RFID system can also reduce business costs, by indentifying the position of goods and picking carts. This study proposes an artificial immune system (AIS)-based fuzzy neural network (FNN), to learn the relationship between the RFID signals and the picking cart’s position. Since the proposed network has the merits of both AIS and FNN, it is able to avoid falling into the local optimum and possesses a learning capability. The results of the evaluation of the model show that the proposed AIS-based FNN really can predict the picking cart position more precisely than conventional FNN and, unlike an artificial neural network, it is much easier to interpret the training results, since they are in the form of fuzzy IF–THEN rules. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Radio frequency identification (RFID) is reputed to be one of the top important ten technologies, in this century (Ranky, 2006). It has a role in the lives of many people and is used in many fields. Enterprises have come to understand influence of RFID and the traditional barcode, on the manufacturing and supply chain, so old equipment has been gradually made redundant, as RFID systems are introduced, which the easy storage of and access to logistics information. The entire process, from raw materials to the sale of products, has been informationalized. This not only reduces the cost of holding stock, but also ensures the safety of products, which enhances the reputation of enterprises. RFID can be used over a long distance, has a high memory capacity and high repeated availability. Logistics organizations, parking lots, libraries, and medical organizations all employ RFID technology. In addition to being utilized to record product information, many scholars have studied the sensory features of RFID, for the identification of the position of an object (Ni, Liu, Lau, & Patil, 2003). If RFID can accurately sense the position of products, high-cost positioning devices will become redundant and existing tags can be used to identify an object’s

q

This manuscript was processed by Area Editor Satish Bukkapatnam.

⇑ Corresponding author. Tel.: +886 2 27376328.

E-mail address: [email protected] (R.J. Kuo). 0360-8352/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cie.2012.06.006

position and the cost associated with the estimation of an object’s position will be greatly reduced. Many studies have considered the introduction of wireless location methods, but all have revealed problems with accuracy, when complicated environmental variables are encountered. Some advanced positioning devices can be used to solve positioning problems in warehouses, but these are always very costly. Therefore, this study proposes the use of the relatively lower cost RFID, to develop a positioning method. In view of the excellent performance of artificial immune systems (AIS), in many fields (Hart & Timmis, 2008), this study develops a fuzzy neural network (FNN) (Lin & Lee, 1991), combined with AIS, to offer a solution to the problem of position estimation, since FNN has shown promising results, in control problems. The efficiency of this method is verified by collecting data from real applications. Received Signal Strength Indication (RSSI), measured by RFID, serves as training data and after training, using the proposed method, a new group of RSSI’s are recorded, for a model constructed using calculation of the signal features. Based on the estimated values, an object’s position can be identified, which can help logistics enterprises to plan picking routes and increase picking efficiency. The remainder of this paper is organized as follows. Section 2 briefly provides a review of the literature from related areas. Section 3 presents the proposed positioning system, which uses AIS-FNN and the results of simulation are presented, in Section 4.

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Section 5 presents the model evaluation results, for a RFID-based positioning system. Finally, concluding remarks are made, in Section 6. 2. Literature review This section will provide a general background of RFID, fuzzy neural networks, artificial immune systems, and applications of soft computing techniques to fuzzy neural networks. 2.1. Radio frequency identification (RFID) RFID employs radio waves to identify an object. A typical RFID system consists of four parts: a reader, a tag, an antenna and a host computer system (Ranky, 2006; Shepard, 2005). Current positioning systems can be divided into indoor and outdoor types. The best known outdoor systems are the Global Positioning System (GPS) and car navigation systems, combined with an electronic map. Common indoor systems are infrared, ultrasonic, and wireless local area network (WLAN) positioning systems. RFID has been extensively studied, in recent years. Compared to outdoor positioning systems, indoor positioning systems are easily affected by the ambient environment, during the measurement of signals. This may cause instability and greater variation in signals. Moreover, indoor spaces are confined, so accurate positioning is more difficult, but also more important. A positioning system provides the actual position information of an object in a specific space, according to the space’s features. For a RFID system, the specific space is the signal range of the wireless access points, arranged by an organization, also called the RFID signal space. The features of this space are collectively referred to as the features of RFID wireless signals, which can be RSSI, or features of wireless signals, calculated according to strength, such as the difference in signal between two wireless points, or the decrease in the strength of a RFID signal, due to obstruction by an object. Recently employed positioning techniques include Triangulation, Scene Analysis and Proximity, etc. (Hightower & Borriello, 2001; Ni et al., 2003). 2.2. Fuzzy neural networks (FNNs) An artificial neural network (ANN) is a system derived from neurophysiology models. In general, it consists of a collection of simple, nonlinear computing elements, whose inputs and outputs are tied together, to form a network (Rumelhart, Hinton, & Williams, 1986). However, a disadvantage of ANN’s, which is an impediment to their more widespread acceptance, is the absence of a capability to explain to the user, in a form comprehensible to humans, how the network arrives at a particular decision. Neither can one comment about the knowledge encoded within the black (Mitra & Hayashi, 2000). On the other hand, fuzzy modeling (Zadeh, 1994), which is used to fuse decisions from different variables, requires an approach that learns from experience (i.e. data collected in advance). In an innovative approach, ANN learning algorithms have been applied to enhance the performance of fuzzy systems. Fuzzy IF–THEN rules are generated and adjusted by these learning methods, using numerical data. Fuzzy control, based on a Takagi–Sugeno (TS) fuzzy model (Takagi & Sugeno, 1985) has been used widely to control nonlinear systems, since a TS fuzzy model efficiently represents a nonlinear system with a set of linear subsystems. Lin and Lee (1991) proposed the so-called Neural-Network-Based Fuzzy Logic Control System (NN-FLCS), shown in Fig. 1 (Buragohain & Mahanta, 2008). They introduced the low-level learning power of ANN’s into

Fig. 1. NN-FLCS structure.

fuzzy logic systems and imbued the normal connectionist architecture with a high-level, human-understandable meaning. There are five layers in NN-FLCS. They are discussed as follows: (1) First (input) layer. In this layer, every node represents an input linguistic variable. Ag represents the gth input linguistic variable, defined as:

O1g ¼ xg :

ð1Þ

O1g

is the output of the gth node of the first layer and xg is the actual input value of the gth linguistic variable. (2) Second (normalization) layer. The purpose of second layer is to map the input variable to the fuzzy set. The output value is in [0, 1], which is defined as:

O2h ¼ lA~ h ðxÞ;

ð2Þ

~ h is one of the linguistic terms of Ag. The number of linguistic A ~ h and x is terms of Ag is ag, lA~h ðxÞ is a membership function of A PM its corresponding input value. This layer has i¼1 ai nodes, in total. lA~h ðxÞ is defined as:

(  2 ) x  mh ; lA~h ðxÞ ¼ exp 

rh

ð3Þ

where {mh, rh} is the set of adjustable parameters, rh is the width of the Gaussian function and mh represents the medium position of the Gaussian function. (3) Third (rule) layer. The nodes of the third layer are responsible for calculating the firing strength of the fuzzy rules. In other words, the third-layer nodes apply the fuzzy intersection operator, AND, to translate the related accommodating degree into firing strength. The calculation is as follows:

O3i ¼

n Y

ðO2h Þ;

ð4Þ

h2C i

where h e Ci represents all of the nodes in the second layer that are connected to the ith node of the third layer. The multiplication is similar to a pairing problem.

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(4) Forth (output condition) layer. This structure applies the inference model of Mamdani. According to this inference model, this layer belongs to the OR operation of fuzzy inference. The output is defined as follows:

O4j ¼ maxðO3i Þ;

ð5Þ

i2C j

where i e Cj represents the nodes in the third layer that are connected to the jth node, in the fourth layer. (5) Fifth (output) layer. The purpose of this layer is defuzzification. The equation employed is as follows:

P

4 j2C k Oj

yk ¼ O5k ¼ P

rbj mbj

4 j2C k Oj mbj

;

ð6Þ

where yk is the output value of the kth linguistic variable, j e Ck represents those nodes in the forth layer that are connected to the kth node, in the fifth layer and {rbj, mbj} is the set of Gaussian membership function parameters, standard deviation and mean. Jang (1993) also proposed a different FNN, called an Adaptive Network-Based Fuzzy Inference System (ANFIS). ANFIS implements a Sugeno-like fuzzy system, in a five-layer network structure. Back-propagation is used to learn the antecedent membership functions, while a least mean squares algorithm determines the coefficients of the linear combinations in the consequences of the rule. In ANFIS, the rule-base must be known in advance. ANFIS adjusts only the membership functions of the antecedent and consequent parameters (Shoorehdeli, Teshnehlab, & Sedigh, 2009). Kuo and Cohen (1998) introduced a feed-forward ANN into fuzzy inference, represented by a TS model. The previously mentioned FNNs are only appropriate for numerical data. However, expert knowledge is always of the fuzzy type, so some researchers have attempted to address this dichotomy. Ishibuchi, Fujioka, and Tanaka (1993) and Ishibuchi, Kwon, and Tanaka (1995) proposed learning methods for ANN’s that utilized not only numerical data, but also expert knowledge, represented by fuzzy IF–THEN rules. Lin and Lu (1995) also presented an FNN, capable of handling both fuzzy inputs and outputs. Meanwhile, Buckley and Hayashi (1994) surveyed recent results for learning algorithms and applications for FNN’s and Buckley introduced several methods for error back-propagation learning algorithms. Kuo and his colleagues (Kuo, Chen, & Hwang, 2001; Kuo, Horng, & Hwang, 2010) further applied different soft computing techniques, including genetic algorithms and particle warm optimization techniques, to the FNN presented by Ishibuchi et al. (1993). 2.3. Artificial immune system Farmer, Packard, and Perelson (1986) suggested a dynamic model of an immune system, based on immune network theory, and discussed the links between an immune system and other artificial intelligence methods. This work represented the beginning of artificial immune system (AIS) research. Dasgupta (2006) suggested a system for the analysis of the differences and similarities between AIS and artificial neural systems. They are similar in their units, number of units, interaction, mode identification, task execution, memory learning and system robustness and different in their system distribution, communication between units and system control. It was noted that the natural immune system was a crucial source of inspiration for the artificial intelligence method. Gasper and Collard (1999) considered diversity as the basic feature of self-adaptive dynamics. AIS’s, which have been applied in many

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areas, represent a kind of optimization method, which is better than GA, for the maintenance of diversity (Hart & Timmis, 2008). In an AIS, an antigen is any foreign substance that causes the immune system to produce an antibody against it. When antigen, A, enters into a body, for the first time, it takes time for multicellular organisms to produce an antibody. When antigen, A, enters into the body for the second time, multicellular organisms immediately produce antibodies and the antibody concentration is much higher than it was, for the first attack. A certain concentration of antibodies remains and does not decrease, as it does when the antigen enters the body for the first time. Immune algorithms are high-efficiency optimization algorithms, based on the immune theory, and are an important element in the study of AIS theory. They can be divided into population-based algorithms and network-based algorithms. The former has no direct link with the elements of the system, but the elements of the system have direct interaction with the system environment, so the elements connect with each other in an indirect manner. The latter is just the opposite, where the algorithm has interaction with some, or even all of the elements (Hofmeyr & Forrest, 2000; Timmis, Neal, & Hunt, 2000). de Castro and Von Zuben (2000, 2002) suggested a cloning selection algorithm, based on an antibody cloning selection mechanism. The cloning selection algorithm consists of six steps. Each execution can produce a new generation of immune cells. The cloning selection algorithm process is as follows: (1) A new generation set is a subset of M-cells, with newly produced population, R. (2) Select Sn individuals, with high affinity. (3) Clone Sn individuals, to form a new clone population, C. The higher the affinity of the antibody, the larger is the size of the individuals in the clone population. (4) Determine mutation rate, according to affinity, and produce mature antibody, C. (5) Reselect C and choose the members with a better affinity to form M-cells. Members of the M-cell can be replaced by members with a high affinity for C. (6) Generate Rnew new antibodies, in order to replace antibodies of low affinity, in the R, and maintain diversity. 2.4. Application of soft computing techniques to FNN Due to the complex training process for FNN’s, soft computing techniques, including genetic algorithms, particle swarm optimization, artificial immune systems and ant colony systems, have been employed to determine their weights and fuzzy membership function parameters (Mitra & Hayashi, 2000). Ishigami, Fukuda, Shibata, and Arai (1995) proposed an autotuning method, for the FNN, using GA. The new tuning method constructs the minimal and optimal structures of the fuzzy model. It can solve the problem of the convergence of tuning, using conventional methods, depending on the initial conditions of the fuzzy model. Shimojima, Fukuda, and Hasegawa (1995) presented a genetic algorithm based FNN, with an adaptive membership function, rules and structure. Kumar and Garg (2004) developed methodologies to learn and optimize fuzzy logic controller parameters, based on ANN and GA. These were used to control an inverted pendulum. The results for three different fuzzy logic controllers, developed with the help of iterative learning, from operator experience, GA and ANN, were compared. As well as the application for a controller, a similar concept was also applied to design (S. Kumar, 2005). The evaluation of each individual design candidate, in terms of its ability to meet the demands of the marketplace, is a crucial step in the conceptual design stage. Consequently, Hsiao and Tsai (2005) proposed a method

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that enables an automatic product form search, or product image evaluation, by means of GA-based FNN. Östermark (2000) proposed a multi-group classification algorithm, based on a hybrid genetic fuzzy neural net (GFNN) framework. It aggregated the signal inherent in the fuzzier, using a suitable Tnorm, and transmitted it to a defuzzifier. The defuzzifier aggregates the predicted group membership, using a suitable conorm. If misclassifications occur, during training, the membership functions of both the fuzzier and the defuzzifier are adapted using a systematic, robust procedure. The algorithm was successfully tested, with real economic data. Particle swarm optimization (PSO) is inspired by observations of the social behavior of birds and it has been applied in solving optimization problems. The advantages of PSO are fast convergence, fewer parameters, ease of discovery of the global optimum, its ease of understanding and simplicity of implementation. One of FNN’s biggest problems is that too many rules result in extended computing time. He et al. (1998) used PSO to extract rules from FNN. The network parameters, including the necessary membership functions of the input variables and the consequent parameters, are tuned and identified using a modified PSO, which uses the best current performance of each particle’s neighbors to replace the best previous one and uses a non-accumulative rate of change to replace the accumulative one, to accelerate the search procedure. The trained network is then pruned, so that the general rules can be extracted and explained. Lin, Liu, and Lee (2008) proposed a functional-link-based neural fuzzy network (FNFN), combined with immune particle swarm optimization (IPSO), to solve prediction and control problems. The IPSO employed the advantages of PSO to improve the mutation mechanism of the immune algorithm. Another study, which examined hybrid AIS, used Learning Vector Quantization (LVQ) and fuzzy set theory to present a new supervised learning method (HFNINME) (Izadinia, Sadeghi, & Ebadzadeh, 2009). This model was compared with other algorithms. The results of the experiments revealed that the proposed method produced a parsimonious classifier that could classify data more accurately and more efficiently. Ant colony systems have also been employed to generate appropriate fuzzy control inputs. Chen, Leu, Wang, and Chen (2010) used ant colony optimization to tune the parameters of a FNN. Using computer simulation, the experimental results show the feasibility and effectiveness of the proposed method. In one application of this research, Bao and Lin (2009) used ant colony optimization control for a FNN, in a freeway entrance ramp. The advantages of the proposed model are fast convergence, reduced computing time, good quality of control, and stabilization of the traffic flow density for the main line.

3. Methodology Some methods of wireless positioning still have an accuracy problem, when complex environments are encountered. If more advanced positioning equipment is used to solve this problem, the cost is very high. This study aims to propose a less expensive positioning system, which uses RFID. It is proposed that an AIS-based FNN (AIS-FNN) formulates the relationship between RSSI signals and a picking cart’s position. Unlike ANN, the advantage of FNN is that the fuzzy inference rules can be explained. This allows the causal relationship to be more easily understood by users. Using the trained AISFNN, the position of the cart can be estimated, as new data is captured by the RFID reader. Then, the system can use this information to plan the picking route for the picking cart, to shorten the picking route. This information can be stored in the built-in chip of the RFID reader.

The proposed RFID-based positioning system consists of four parts. The first part is the collection of RFID signal data, while the second part is RFID data transformation. The third part uses the collected data to train the proposed AIS-FNN to establish the initial network parameters. The final part fine-tunes the network parameters, using the steepest descent method, similar to the error back-propagation learning algorithm. The research framework is illustrated in Fig. 2. A detailed discussion of each part follows. 3.1. RFID data collection Nowadays, most wireless positioning systems use the Received Signal Strength Indication (RSSI) to estimate performance. When Radio Frequencies pass through the air, the signal strength decreases, because of the distance and the nature of the transmission media. Using this index, the range of the signal can be estimated, regardless of the signal direction. This study employs RSSI to estimate the distance between the signal source and the receiver. Generally speaking, a single receiver is not enough to determine a position accurately. Normally, three or more fixed-point receivers are required, to estimate distance. 3.2. RFID data transform In the telecommunications field (IEEE 802.11 System), RSSI represents a received radio signal strength index. It has two elements. One is the absolute value, in dBm, which describes the power, and the other is the partition of the power. The unit of the value obtained from the subtraction of absolute value for the power is db. The receivers used in this study are OMRON V750-series UHF RFID Systems. Their unit of measure is dBm and their range is from 70 to 40 dBm. If the dBm value is close to 0, the signal strength of RSSI index is strong. Before feeding the data into AIS-FNN, the data must be normalized to [0, 1], according to following equation:

Ri ¼

X i  X min ; X max  X min

ð7Þ

where Xmin is the minimum of the data, Xi, for all i, Xmax is the maximum of the data Xi, for all i and Xi is the ith data element of data X. 3.3. Artificial immune system-based fuzzy neural network This subsection presents an AIS-based FNN, to solve the optimization problem. An optimization problem can be treated as an antigen and the feasible solutions as antibodies. The goal of AIS is to eliminate the antigen with self-generated antibodies. The data must be encoded, before evolution, by the AIS. The most common methods are binary encoding and floating-point encoding. The latter is more efficient than the former, because the former must still transform the solutions into binary data, which can cause the distortion of the result and increase the computational time. Floating-point encoding generates solutions, randomly, according to the range of the constraints. In this study, all solutions are compiled into a series of antibodies, to solve the problems. The AIS is able to search for feasible solutions, to find the optimal solution (eliminate antigens with antibodies), using the FNN structure. The memory cells record the current good solutions and search for better solutions. The fitness function, used in this study, is the mean square error (MSE) between the target and actual outputs. It is used to compare the degree of convergence. In an immune system, the foreign substances are antigens. When antigens enter an organism, antibodies are generated, which try to kill the antigens. This study views the

R.J. Kuo et al. / Computers & Industrial Engineering 63 (2012) 943–956

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Fig. 2. Research framework.

original random population as original antibodies and the fitness function as the antigen. Fig. 3 illustrates the AIS-FNN framework. The corresponding procedures are as follows:

The initial antibodies are generated randomly. Since the purpose of AIS is to find the FNN weights and Gaussian fuzzy numbers, including the mean, l, whose membership function is equal to 1, standard deviation, r, and the weights of rules, the encoding of an antibody is Pi = [l, r, W1, W2, . . . , Wn], where n is the total number of rules in FNN. Step 2: Calculate the output of new antibodies and memory cells, for the FNN. FNN uses an error function for correcting and convergence, similarly to an error back-propagation learning algorithm. Firstly, having generated antibodies, in the last step, calculate the membership function, using:

(  2 ) x  mh : rh q

O3i ¼

n Y

ðO2h Þ:

ð9Þ

h2C i

Step 1: Generate R set of new antibodies, randomly.

lA~h ðxÞ ¼ exp 

Then, calculate the firing strength of fuzzy rules, using the equation:

ð8Þ

Thereafter, calculate the OR operation, of fuzzy inference, by the equation:

O4j ¼ maxðO3i Þ:

ð10Þ

i2C j

The equation:

P

4 j2C k Oj

yk ¼ O5k ¼ P

rbj mbj

4 j2C k Oj mbj

;

ð11Þ

is for defuzzification. Then, the outputs of new antibodies and memory cells can be obtained through above calculations. Step 3: Calculate the affinity between each antibody and antigen. The distance between the target output and the actual output of the FNN is represented by the MSE between the target and actual outputs. Then, it is transformed into an affinity. A smaller MSE

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1.Generate antibodies randomly in every iteration (Rnew-fuzzy numbers and weight)

2.1 New antibody (R-initial fuzzy numbers and weights) Result: Output the best antibody of the Mcell(the last fuzzy numbers and weights)

2.2 Memory cell (Mcell)

9.3 Select the best Sn* set of antibodies to replace the memory cells

Yes 10. Reaches the maximum number of iteration?

No 9.2 Calculate the affinity

3.1 Calculate the output of FNN

9.1 Re-calculate the output of FNN

3.2 Calculate the affinity

4.Select the best Sn set of antibodies

6.1 The rest of antibodies

5.Clone Sn set of antibodies into C set.

Choose 0.5*Cr set of antibodies randomly

Choose 0.5* Cr set of antibodies randomly

6.2 Crossover then generate Cr set of offsprings

7.1 According to the affinity to generate C* set of antibodies

7.2 According to the default value of mutation rate to generate Cr* set of antibodies

8. C*+ Cr* set of antibodies

Fig. 3. AIS-FNN framework.

corresponds to a larger affinity. A larger affinity means a greater opportunity to be cloned. The transformation function is as follows:

ðAgÞi ¼

1 1 þ Di

PN Di ¼

k¼1

where

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðdk  ok Þ2 N

ð12Þ

Step 4: Select Sn set of antibodies, which have the highest degree of affinity. According the affinities obtained from Step 3, select the highest affinity Sn antibodies, for cloning. Step 5: Clone Sn set of antibodies into C set.

;

ð13Þ

N is the number of data elements, dk is the output value of the kth data element and o is the actual value of the kth data element.

Cloning is the process of copying creatures, using biotechnology. In AIS, antibody cells are copied, because this increases the competitiveness of good antibodies. Different amount of antibodies are

R.J. Kuo et al. / Computers & Industrial Engineering 63 (2012) 943–956

Fig. 4. The RFID reader and antenna used in the experiments.

Fig. 5. Experimental setup for scenario 1.

Fig. 6. Membership functions for scenario 1.

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R.J. Kuo et al. / Computers & Industrial Engineering 63 (2012) 943–956 Table 1 Training and test results of different FNN’s for scenario 1.

Training MSE Test MSE Average accuracy rate

AIS-FNN

GA-FNN

NN-FLCS

0.005031 0.003615 100%

0.009044 0.008469 92.6%

0.004912 0.004601 95.6%

Table 2 Fuzzy IF–THEN rules for scenario 1. Fuzzy rule

Fig. 7. Convergence curve of AIS-FNN for scenario 1.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

IF (input, RSSI values) x1

x2

x3

x4

THEN (output, cart position) y

Small Medium Medium Small Small Small Small Small Small Medium Medium Medium Medium Small Medium Medium Large

Large Large Large Medium Medium Medium Medium Large Large Large Medium Small Medium Medium Medium Large Small

Small Small Medium Small Medium Small Medium Large Large Large Large Medium Medium Small Small Small Medium

Medium Medium Medium Small Small Medium Medium Small Medium Medium Small Medium Medium Large Large Large Small

1 2 2 3 3 3 3 4 4 4 5 6 6 7 8 8 9

Fig. 8. The convergence curves of different FNN’s for scenario 1.

Fig. 10. Experimental setup for Scenario 2.

ðAgÞi The clone amount of ith antibody ¼ C  P : i ðAgÞi

ð14Þ

The value of the result must be rounded, to avoid a non-integer solution. Step 6: Implement crossover, by selecting the antibodies with the highest and second highest affinities, to generate Cr set of antibodies. Fig. 9. The accuracy rates of different methods for scenario 1.

cloned, depending on the affinity. In short, when antibody, A, has a greater affinity, the ratio of cloned cells is higher. The reproductive rate is proportional to the affinity. The C set of antibodies is generated, after cloning.

Crossover is an information exchange mechanism. Its purpose is to exchange the gene, to produce better offspring. This research uses a clone with crossover. Cloning can increase the concentration of good antibodies. Assume there is Cr set of antibodies, after crossover. Choose Cr  0.5 set of antibodies as the best antibodies and

R.J. Kuo et al. / Computers & Industrial Engineering 63 (2012) 943–956

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Fig. 11. Membership functions for scenario 2.

also the second best antibodies and then perform crossover. According to the uniform crossover proposed by Ddewuya (1996) using a random value for b, between 0 and 1, can avoid bordercrossing due to too much mutation. The crossover is as follows:

PO1 ¼ ð1  bÞ  P1 þ b  P2 PO2 ¼ b  P1 þ ð1  bÞ  P2 ;

and

ð15Þ ð16Þ

where P1 and P2 are the parents, selected from the best and second best antibodies. Cr set of offspring is generated. The offspring mutate, as previously mentioned. Step 7: Cell mutation (the mutation rate of group C depends on the affinity and the mutation rate of group Cr depends on the default value.) In a biological immune system, mutation occurs when the protein in the antibody changes. In AIS, mutation occurs when the fuzzy numbers, or weights in an antibody change. Mutation may cause an antibody to become better or worse, but it is also viewed as a driving force for convergence. Bad mutations are eliminated, by selection, but good mutations thrive. Appropriate mutation is necessary. The difference between AIS and the genetic algorithm is that genetic algorithm has a fixed mutation rate, but AIS does not. The mutation rate of AIS is inversely proportional to the affinity. According to the research of Ramaswamy, Venayagamoorthy, and Balakrishnan (2007), the transformation function is as follows:

ai ¼

  1

q

expððAgÞi Þ;

antibody will mutate. If not, the antibody will not mutate. Assume that there is C set of antibodies, after cloning, and C set of mutation rates, after transformation. Generally, there are many methods of mutation. The most common methods select which parent to mutate and then replace the parent by a random number, but this kind of method does not change the structure. Although the method is feasible, the efficiency may not be acceptable. This study’s method of mutation randomly selects n set of parents, to mutate into the antibody. The ith antibody’s upper and lower bounds are UBi and LBi. Assume that k is the mutation point, so the new Wk-new can be obtained, as follows:

W knew ¼ LBi þ RandðUBi  LBiÞ;

where Rand is a random number between 0 and 1. This method is termed ‘‘Uniform mutation.’’ The old value is replaced by a new value.

ð17Þ

where ai is the mutation rate of the ith antibody. From this equation, in which q is a given constant, it can be seen that the antibody with the greater affinity has the smaller mutation rate. The goal is to limit the mutation to some range and avoid that the affinity of Ag becoming equal to 0. Then, a random number, ranging between 0 and 1, is generated. If the random number is smaller than ai, the

ð18Þ

Fig. 12. Convergence curve of AIS-FNN for scenario 2.

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R.J. Kuo et al. / Computers & Industrial Engineering 63 (2012) 943–956 Table 3 Training and test results of different FNN’s for scenario 2.

Training MSE Test MSE Average accuracy rate

AIS-FNN

GA-FNN

NN-FLCS

0.001158 0.000984 100%

0.004418 0.007334 70.7%

0.003194 0.003969 88.7%

3.4. FNN fine-tuning

Fig. 13. The convergence curves of different FNN’s for scenario 2.

Step 8: Combine C and Cr. After mutation, combine C and Cr sets of antibodies. Step 9: Re-calculate the affinities and select the Sn set of antibodies with the highest affinity, to replace the memory cells. Calculate the affinities of the new antibodies and memory cells. Because there are no good antibodies in the first iteration, the antibodies of the memory cell, in the first iteration, are generated randomly. After cloning and mutation, calculate the affinity, again, to select the antibody with better affinity. Then, the antibodies with better affinity replace the memory cells. The antibodies with high affinity are not easily replaced. They replicate more good antibodies, in the cloning step, to increase the concentration of good antibodies. Step 10: Repeat Step 1 to Step 9, until the specified number of iterations is satisfied. When the pre-specified number of iterations is reached, then use the best antibody in memory cell for the initial weights of FNN. The method combining AIS and FNN optimizes the FNN, to obtain the best weights and can be used for classification and forecasting applications. The difference between FNN and back-propagation is interpretability. The rule inference can identify the factor with the greatest impact.

This final section describes the process of the AIS-FNN, using the gradient steepest descent method, to fine-tune the FNN parameters, obtained from the AIS. Generally, using AIS to train FNN avoids acceptance of the local optimal solution and also provides the best solution, through error back-propagation-type learning. The tuning of FNN parameters has two elements, the IF and THEN operators of fuzzy inference. The IF element initializes the mean (center) and standard deviation (width) of the Gaussian function. The THEN element has only one output. Conventionally, the most frequently used method is the gradient steepest descent method. This is also the most common method in error back-propagation learning algorithms. A FNN reaches error convergence by tuning the fuzzy numbers and weights of the membership function. All of the training data is input, to obtain an output. The error between the target value and the output value is determined and the revised value, which is calculated by Steepest Decent, for every layer, is returned. Tuning of the weights and variables of membership function continues, until the error is less than a preset value, or the number of iterations reaches the preset limit. The error function is as follows:

Ep ¼

E¼

1X ðdpk  Opk Þ2 2 k

and

ð19Þ

X Ep ;

ð20Þ

P

where P is the total amount of training data, k is the number of output nodes, Ep is the output error of every data element, E is the total error, for all training data, and dpk is the target value of the kth output layer. The goal is to find the optimal solution, to minimize E. Tuning the weight decreases E, so:

EðW þ DWÞ < EðWÞ

ð21Þ

DW represents the revised value of weights. This is the adjusting function.

Table 4 Fuzzy IF–THEN rules for scenario 2. Fuzzy rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Fig. 14. The accuracy rates of different methods for scenario 2.

IF (input, RSSI values) x1

x2

x3

x4

THEN (output, cart position) y

Large Large Large Small Small Small Small Large Large Large Medium Large Large Large Large Large

Medium Large Large Large Large Medium Large Small Small Small Small Small Medium Large Large Large

Medium Medium Large Medium Medium Large Large Large Small Medium Large Large Small Small Medium Large

Large Large Large Large Large Large Large Medium Large Large Large Large Large Large Small Small

0 0 0 1 1 1 1 2 2 2 2 2 3 3 4 4

R.J. Kuo et al. / Computers & Industrial Engineering 63 (2012) 943–956

W ij ðt þ 1Þ ¼ W ij ðtÞ  g

@E þ aDW ij ðtÞ @W ij

953

ð22Þ

training epochs and @ 2ij is the error signal of the jth training data of the ith node of the 2nd layer.

ð23Þ

d2ij ¼

L3 @omj @Ej X @Ej ¼  ; @o2ij m¼1 @o3mj @o2ij 3

DW ij ðtÞ ¼ W ij ðtÞ  W ij ðt  1Þ

Use the same method to tune the variables of the membership function {m, r}.

mij ðt þ 1Þ ¼ mij ðtÞ  gd2ij

rij ðt þ 1Þ ¼ rij ðtÞ  gd2ij

2ðxi  mij Þ

r2ij

2ðxi  mij Þ2

r2ij

þ aDmij ðtÞ

ð26Þ

where L3 is the total number of 3rd node and o2ij is the output value of the ith node of the 2nd layer.

ð24Þ 4. RFID-based positioning system

þ aDrij ðtÞ

ð25Þ

In the above equations, g is the learning rate that controls the adjustment of the steepest decent. Its value is usually between 0 and 1. When g is large, the search speed is high, but divergence is more likely. When g is small, the search speed is slow, but divergence is not so likely. a is the momentum coefficient, t is the

The results obtained from the simulation demonstrate that the proposed AIS-based FNN can be practically applied in an RFIDbased positioning system. In this study, the RFID reader employed was an OMRON V750-series UHF RFID System, as illustrated in Fig. 4. The RSSI unit, displayed by this device, was dBm, ranging from 70 to 40 dBm. The closer the reading is to 0 dBm, the stronger is the RSSI signal strength. Due to the differing conditions of different environments, the value measured by the RSSI was not

Fig. 15. Experimental setup for scenario 3.

Fig. 16. Membership functions for scenario 3.

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R.J. Kuo et al. / Computers & Industrial Engineering 63 (2012) 943–956

inversely proportional to distance, so the signal value was repeatedly tested, throughout network training. 4.1. Scenario 1 Scenario 1, as shown in Fig. 5, was applied and an open field was assumed, which could be either a warehouse or a factory area. This area was divided into a 3  3 matrix block. To increase the accuracy of the FNN, a wireless RF receiver was connected to four receiving antenna, fixed in four planes. Each position has four RSSI values, depending on the different distances between the antennae and the objects. In this experiment, each grid represented the position of a pallet, in the scenario. The RSSI signals, collected from four antennas, were regarded as the input values for the FNN, where floor numbers 1 through 9 were set as 0.1–0.9, so a consecutive value and target value was set, for the FNN. The simulation experiment yielded 270 data elements, including RSSI values and picking cart positions (30 data elements for each position), collected from the four antennae. The test data were arranged randomly and verified, using a 10-fold cross-validation method. Figs. 6 and 7 illustrate the trained membership functions, for each input variable, and the MSE values, for scenario 1, after 500 training cycles, respectively, where 90% is used for training and 10% for testing. The neural cells in the 2nd layer of the FNN can be divided into great, middle, and small levels, according to RSSI signal strength. The convergence curves of the MSE and the accuracy rate are shown in Figs. 8 and 9, respectively. Table 1 presents MSE values and mean accurate rates, for different FNN’s, using the 10-fold cross-validation method. The prediction accuracy of the proposed method averaged 100% and the smallest training MSE and test values were 0.005031 and 0.003615, respectively. The trained fuzzy rules are summarized in Table 2.

4.3. Scenario 3 Scenario 3, shown in Fig. 15, extended the scope of scenario 2. A group of shelves was added, to allow the simulation of a picker walking along the passageway between the shelves, which contained goods. The antenna of the intelligent shelf, on one side, could read the RSSI value from the other side. Using the positions of picker and the RSSI, collected experimentally, AIS-FNN was used to train the environmental model. This simulation experiment collected 150 data elements, in the same way as for experiment 2–30 data elements referring to positions where no picker stood and the remainder being positions where the picker stood. The test data were arranged randomly and a 10-fold cross-validation method was used, to ensure the randomness and independence of the data. Figs. 16 and 17 show the trained fuzzy membership functions and MSE curves, respectively, for scenario 3 data, after 1000 training cycles. Fig. 18 shows the MSE convergence curves for the three FNN models. The AIS-FNN is still trained fastest, with test MSE values of 0.001644 and 0.001115, respectively, as shown in Table 5. From Fig. 19, it can be seen that the accuracy rate of the AIS-FNN reached 100%, after 500 training iterations and the accuracy rates for NN-FLCS and GA-FNN failed to reach 100%, even after 1000 iterations. In the future, if new data are obtained, the position of picker

4.2. Scenario 2 Scenario 2, shown in Fig. 10, took a current intelligent shelf, as an example. This is used to check materials and to monitor the position, quantity, storage time, etc., of goods. The shelf was simulated AIS-FNN was used to construct a model of this environment, using the RSSI values generated by a picker, walking between the reader and the shelf tag. If certain goods are required, the position of the picker closest to the goods is determined by reading the RSSI, for the model. This simulation experiment collected 150 data elements – 30 data elements being positions where no picker stood and the remainder being positions where the picker stood. The test data were arranged randomly and a 10-fold cross-validation method was used, to ensure the randomness and independence of the data. Figs. 11 and 12 show the trained fuzzy membership functions and MSE values, respectively, for scenario 2 data, after 1000 training cycles. The MSE convergence curves and accuracy rate curves are illustrated in Figs. 13 and 14, respectively. The MSE curves show that the proposed AIS-FNN converges quickly. Fig. 14 shows that accuracy rate of AIS-FNN was 100% after only 350 iterations. The accuracy rate for NN-FLCS reached 100%, only after 500 training iterations and GA-FNN could not achieve 100% accuracy, even after 1000 iterations. Table 3 lists the final MSE values, for different FNN models. The fuzzy rules are summarized in Table 4. In the future, if new data are obtained, the position of picker can be estimated by the AIS-FNN, using the trained weights and fuzzy membership functions. The results from scenario 2 indicate that the AIS-FNN was effective in its application to a RFID intelligent positioning system.

Fig. 17. Convergence curve of AIS-FNN for scenario 3.

Fig. 18. The convergence curves of different FNN’s for scenario 3.

R.J. Kuo et al. / Computers & Industrial Engineering 63 (2012) 943–956 Table 5 Training and test results of different FNN’s for scenario 3.

Training MSE Test MSE Average accuracy rate

AIS-FNN

GA-FNN

NN-FLCS

0.001644 0.001115 100%

0.00372 0.003727 80%

0.002994 0.002604 91.3%

955

Therefore, in the real applications, warehouse can be divided into some blocks. Each block employs our proposed system to determine the staff location. Then, it will not be very complicated and the uncertainty can be mostly overcome. 5. Conclusions This study presents an AIS-based FNN for use in a RFID-based positioning system. The proposed AIS-FNN can learn the relationship between the RSSI values and a picking cart position. Its performance is better than those of GA-FNN and NN-FLCS, for the three scenarios tested. The AIS-FNN results, which are in the form of fuzzy IF–THEN rules, can be easily interpreted. Once the picking cart position is known, the picking route can be planned, in order to provide the shortest picking distance. Due to FNN characteristic, once the warehouse layout is changed, then it is necessary to recollect the data and retain the FNN. In future, other soft computing techniques, such as particle swarm optimization, might be integrated into the heuristics, in order to provide better estimation. Rule pruning might also be considered. Acknowledgements

Fig. 19. The accuracy rates of different methods for scenario 3.

This study was financially supported by the National Science Council of the Taiwan Government, under contract number NSC99-2221-E-011-057-MY3. This support is greatly appreciated. References

Table 6 Fuzzy IF–THEN rules for scenario 3. Fuzzy rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

IF (input, RSSI values) x1

x2

x3

x4

THEN (output, cart position) y

Large Large Large Large Large Large Large Large Large Large Large Large Large Large Large Large Large Small Small Small

Medium Large Medium Large Medium Large Large Medium Medium Large Medium Large Medium Large Small Small Small Large Medium Large

Large Large Large Large Large Large Medium Small Medium Medium Small Small Medium Medium Medium Large Large Medium Large Large

Large Large Small Small Medium Medium Small Medium Medium Medium Large Large Large Large Medium Medium Large Large Large Large

0 0 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 4 4 4

can be estimated by AIS-FNN. Table 5 lists the final MSE values, for different FNN models. The fuzzy rules are summarized in Table 6. The results from scenario 3 show that AIS-FNN is eminently suitable for use in a RFID intelligent positioning system. The data, read by RFID antennae, can be transformed into qualitative data, expressed in terms of large, medium, or small, according to normalized RSSI values. Table 6 lists the results of the IF– THEN rule, after FNN training. The position, 0, is the position at which no picking person stands. Scenario 3 differed from scenario 2 in that the picker was positioned in passageway, between two shelves. In experiment 3, the purpose of placing goods on the new shelves was to verify whether this placement could affect positioning. Based on the verification result for AIS-FNN, the positioning was accurate.

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