- Email: [email protected]
Fuzzy logic, neural networks and soft computing
Microprocossing and Microprogramming 38 (1993) 13 North-Holland
13
Fuzzy Logic, Neural Networks and Son Computing (Abstract) Lotfi
Z. Zadeh
Computer Science Divison and the Electronics Laboratory, Department of EECS, University of California, Berkeley, CA 94720.
The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as softcomputing. The distinguishing characteristic of a soft computing is that its primary aim is to achieve tractability, robustness, low cost and high MIQ (machine intelligence quodent) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought ia an approximate solution to a precisely formulated problem or more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost.
The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory and parts of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application, areas for soft computing are control systems, expert systems, data compression techniques, image processing and decision support systems. It may be argued that it is soft computing -rather than the traditional hard computingthat should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.
13
Fuzzy Logic, Neural Networks and Son Computing (Abstract) Lotfi
Z. Zadeh
Computer Science Divison and the Electronics Laboratory, Department of EECS, University of California, Berkeley, CA 94720.
The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as softcomputing. The distinguishing characteristic of a soft computing is that its primary aim is to achieve tractability, robustness, low cost and high MIQ (machine intelligence quodent) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought ia an approximate solution to a precisely formulated problem or more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost.
The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory and parts of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application, areas for soft computing are control systems, expert systems, data compression techniques, image processing and decision support systems. It may be argued that it is soft computing -rather than the traditional hard computingthat should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.