- Email: [email protected]
Fuzzy sets engineering
NEUROCOMPUTING Neurocomputing 9 (1995) 357-359
Book reviews
Material to be included in this section can be submitted to: Dr. K.J. Cios, University of Toledo, Department of Electrical 43606-3390, USA. Email: [email protected]
F’uzgy Sets Engineering, 0-8493-9402-3
Engineering,
Toledo, OH
by Witold Pedrycz. CRC Press, 1995, pp. 332, ISBN
The phenomenon of the recent plethora of applications of fuzzy sets in various areas of engineering and science has triggered a significant interest among researchers as well as practitioners. The book “Fuzzy Sets Engineering” is about fundamentals and practice of fuzzy sets engineering. The author, Witold Pedrycz, well-known researcher in the field of fuzzy sets and control, exposes the reader to a vast number of problems formulated and solved within the framework of fuzzy set technology. Undoubtedly, the author has accomplished two main goals. On one hand, he has rigorously exposed the fundamentals of fuzzy sets along with their application-oriented methodology. On the other hand, the book shows fuzzy sets as a mature information processing technology being well supported by an advanced computational environment. This is exemplified by a unique concept of fuzzy neural networks. The proposed paradigm of fuzzy neurocomputing is aimed at retaining explicit schemes of knowledge representation (as being specific to fuzzy sets) while augmenting the architectures by significant learning capabilities. Fuzzy neural networks are immensely heterogeneous architectures composed of logic-oriented neurons and exhibiting various functional characteristics. This makes them highly promising especially when available domain knowledge needs to be deployed onto a fuzzy neural network - to facilitate that the topology of the network is selected accordingly and trained parametrically afterwards. This approach may avoid very tedious and overly lengthy training-from-scratch procedures. To give the reader a brief overview of the material covered in the book, let us look at its table of contents. Chapter 1 outlines the principles of fuzzy set-based modeling and simulation environment. Chapter 2 addresses several fundamental issues of interfacing t%zzy models with (numeric) modeling environment. Chapters 3 and 4 deal with fuzzy neural networks and their utilization. Fuzzy controller is studied in Chapter 5. This material is followed by a thorough, in-depth analysis of software development tools used to support the design of fuzzy systems in Chapter 6. Fuzzy control is studied in Chapter 7 while the main aspects of fuzzy information processing are covered in Chapter 8. Finally, fuzzy Petri nets are analyzed in Chapter 9. The book also includes three appendices. 0925-2312/95/$09.50 8 1995 Else.vier Science B.V. Ah rights reserved SSDZ 0925-2312(95)00079-t?
358
Book Reviews
As already mentioned, a substantial portion of the book is devoted to fuzzy neurocomputation. Chapter 3 introduces the basic constructs (processing units) of logic neurons and discusses various architectures including those of logic processors. Subsequently, Chapter 4 entitled “Fuzzy Neurocomputing” provides the reader with a wealth of interesting and diverse problems formulated in the framework of fuzzy neural networks: optimal vector quantization, fuzzy computational memories, decomposition of relations, to name a few. Some further examples of fuzzy neural networks are covered in the subsequent chapters, like fuzzy flip-flops, Petri nets and learning processes, fuzzy controller. The book treats the introductory material on fuzzy sets in a different way than most (if not all) other books in the fuzzy sets area by organizing all pertinent fundamentals in the series of three appendices (A. Fuzq Sets- Notions, Operations; B. Relation and Fuzzy Relations, Fuzzy Relational Equations, Extension Principle; C. Fuzzy Sets and Probability). While somewhat unusual, this is an ideal way of exposing the reader to the necessary fundamentals of fuzzy sets while simultaneously avoiding a lot of the esoteric content not essential to the fundamentals or applications of fuzzy sets. In fact, to gain a good understanding what fuzzy sets are all about and how they work, it is usually enough to go through a brief introduction to the subject, if it is as carefully prepared and well thought as the one presented in this book. In summary, the book is an outstanding and highly valuable contribution to fuzzy neural networks it is strongly recommended to any reader interested in this fascinating realm of knowledge-based neurocomputation. On the whole the versatile format of the book makes it an ideal textbook or a reference source. Rrzysztof Cios
Backpropagation: Theory, Architectures, Applications, by Yves Chauvin and David E. Rumelhart (eds). Lawrence Erlbaum, Hillsdale, NJ, Hove, UK, 1995. ISBN 0-8058-1258-X, pp. 561. In 1986 McClelland and Rumelhart published the famous PDP book. Its chapter eight on backpropagation made history inspiring a wave of interest and research in neural networks (NN). Almost a decade later Chauvin and Rumelhart, acting both as editors and contributors, are publishing a volume devoted to backpropagation. This time there is no breakthrough. The goal of the book is much more moderate. As set forth in the preface it is a progress report on theory, architecture, and applications of backpropagation. The book consists of 15 chapters, in fact a selection of articles prepared by various authors. The first chapter is an introduction. The remaining chapters vary as to their contents and the scope of discussion and can be roughly classified into three categories. Chapters in the first category discuss a single application like phoneme recognition (chapter 21, flare phase control of a landing aircraft (chapter 3), representation of a finite state environment (chapter ll), or fingerprint matching (chapter 14). Chapters in the second category present specific algorithms or architectures and illustrate them using one or several case studies. Papers in this category present the following issues: recurrent backpropagation networks (chapter 4), focused backpropagation algorithm (chapter 5), networks for nonlinear control (chapter 6), distal supervised learning (chapter 71, simple recurrent networks (chapter 9), use of self organization as a first phase in the learning process (chapter 10). Finally, there is a third category of chapters which I would call theoretical. These chapters either present a generalized view on backpropagation, like chapter 8 (Backpropagation: comments and variations) and chapter 15 (Unified perspective on gradient descent learning), or are devoted to a specific theoretical issues, like chapter 12 that presents a mathematical analysis of learning in linear networks, or chapter 13 devoted to computational complexity of learning in recurrent networks. Most of the articles making the volume have never been published before. However, there are also chapters that are either modifications of earlier publications (chapters 9, 12 and 15) or just reprints (chapters 2 and 8) of previous publications. The chapters present more or less the same level of
Book reviews
Material to be included in this section can be submitted to: Dr. K.J. Cios, University of Toledo, Department of Electrical 43606-3390, USA. Email: [email protected]
F’uzgy Sets Engineering, 0-8493-9402-3
Engineering,
Toledo, OH
by Witold Pedrycz. CRC Press, 1995, pp. 332, ISBN
The phenomenon of the recent plethora of applications of fuzzy sets in various areas of engineering and science has triggered a significant interest among researchers as well as practitioners. The book “Fuzzy Sets Engineering” is about fundamentals and practice of fuzzy sets engineering. The author, Witold Pedrycz, well-known researcher in the field of fuzzy sets and control, exposes the reader to a vast number of problems formulated and solved within the framework of fuzzy set technology. Undoubtedly, the author has accomplished two main goals. On one hand, he has rigorously exposed the fundamentals of fuzzy sets along with their application-oriented methodology. On the other hand, the book shows fuzzy sets as a mature information processing technology being well supported by an advanced computational environment. This is exemplified by a unique concept of fuzzy neural networks. The proposed paradigm of fuzzy neurocomputing is aimed at retaining explicit schemes of knowledge representation (as being specific to fuzzy sets) while augmenting the architectures by significant learning capabilities. Fuzzy neural networks are immensely heterogeneous architectures composed of logic-oriented neurons and exhibiting various functional characteristics. This makes them highly promising especially when available domain knowledge needs to be deployed onto a fuzzy neural network - to facilitate that the topology of the network is selected accordingly and trained parametrically afterwards. This approach may avoid very tedious and overly lengthy training-from-scratch procedures. To give the reader a brief overview of the material covered in the book, let us look at its table of contents. Chapter 1 outlines the principles of fuzzy set-based modeling and simulation environment. Chapter 2 addresses several fundamental issues of interfacing t%zzy models with (numeric) modeling environment. Chapters 3 and 4 deal with fuzzy neural networks and their utilization. Fuzzy controller is studied in Chapter 5. This material is followed by a thorough, in-depth analysis of software development tools used to support the design of fuzzy systems in Chapter 6. Fuzzy control is studied in Chapter 7 while the main aspects of fuzzy information processing are covered in Chapter 8. Finally, fuzzy Petri nets are analyzed in Chapter 9. The book also includes three appendices. 0925-2312/95/$09.50 8 1995 Else.vier Science B.V. Ah rights reserved SSDZ 0925-2312(95)00079-t?
358
Book Reviews
As already mentioned, a substantial portion of the book is devoted to fuzzy neurocomputation. Chapter 3 introduces the basic constructs (processing units) of logic neurons and discusses various architectures including those of logic processors. Subsequently, Chapter 4 entitled “Fuzzy Neurocomputing” provides the reader with a wealth of interesting and diverse problems formulated in the framework of fuzzy neural networks: optimal vector quantization, fuzzy computational memories, decomposition of relations, to name a few. Some further examples of fuzzy neural networks are covered in the subsequent chapters, like fuzzy flip-flops, Petri nets and learning processes, fuzzy controller. The book treats the introductory material on fuzzy sets in a different way than most (if not all) other books in the fuzzy sets area by organizing all pertinent fundamentals in the series of three appendices (A. Fuzq Sets- Notions, Operations; B. Relation and Fuzzy Relations, Fuzzy Relational Equations, Extension Principle; C. Fuzzy Sets and Probability). While somewhat unusual, this is an ideal way of exposing the reader to the necessary fundamentals of fuzzy sets while simultaneously avoiding a lot of the esoteric content not essential to the fundamentals or applications of fuzzy sets. In fact, to gain a good understanding what fuzzy sets are all about and how they work, it is usually enough to go through a brief introduction to the subject, if it is as carefully prepared and well thought as the one presented in this book. In summary, the book is an outstanding and highly valuable contribution to fuzzy neural networks it is strongly recommended to any reader interested in this fascinating realm of knowledge-based neurocomputation. On the whole the versatile format of the book makes it an ideal textbook or a reference source. Rrzysztof Cios
Backpropagation: Theory, Architectures, Applications, by Yves Chauvin and David E. Rumelhart (eds). Lawrence Erlbaum, Hillsdale, NJ, Hove, UK, 1995. ISBN 0-8058-1258-X, pp. 561. In 1986 McClelland and Rumelhart published the famous PDP book. Its chapter eight on backpropagation made history inspiring a wave of interest and research in neural networks (NN). Almost a decade later Chauvin and Rumelhart, acting both as editors and contributors, are publishing a volume devoted to backpropagation. This time there is no breakthrough. The goal of the book is much more moderate. As set forth in the preface it is a progress report on theory, architecture, and applications of backpropagation. The book consists of 15 chapters, in fact a selection of articles prepared by various authors. The first chapter is an introduction. The remaining chapters vary as to their contents and the scope of discussion and can be roughly classified into three categories. Chapters in the first category discuss a single application like phoneme recognition (chapter 21, flare phase control of a landing aircraft (chapter 3), representation of a finite state environment (chapter ll), or fingerprint matching (chapter 14). Chapters in the second category present specific algorithms or architectures and illustrate them using one or several case studies. Papers in this category present the following issues: recurrent backpropagation networks (chapter 4), focused backpropagation algorithm (chapter 5), networks for nonlinear control (chapter 6), distal supervised learning (chapter 71, simple recurrent networks (chapter 9), use of self organization as a first phase in the learning process (chapter 10). Finally, there is a third category of chapters which I would call theoretical. These chapters either present a generalized view on backpropagation, like chapter 8 (Backpropagation: comments and variations) and chapter 15 (Unified perspective on gradient descent learning), or are devoted to a specific theoretical issues, like chapter 12 that presents a mathematical analysis of learning in linear networks, or chapter 13 devoted to computational complexity of learning in recurrent networks. Most of the articles making the volume have never been published before. However, there are also chapters that are either modifications of earlier publications (chapters 9, 12 and 15) or just reprints (chapters 2 and 8) of previous publications. The chapters present more or less the same level of