Pergamon Engng Applic. Artif. lntell. Vol. 9, No. 4, pp. 465-466, 1996

Copyright © 1996 Published by Elsevier Science Ltd Printed in Great Britain. All rights reserved

0952-1976/96 $15.D0 + 0.DO

Book Reviews

Neural Network Learning and Expert Systems, by Stephen I Gallant. MIT Press, Cambridge, MA, U.S.A. (1993). $47.50. ISBN: 0 262 07145 2

In the foreword to this book the author sets out a number of goals that he hopes to address. Essentially, these are to provide "a systematic development of neural network learning algorithms", to present what he calls "neural network expert systems" (by which he means hybrid neural network and heuristic rule-based systems), and to do so in such a way as to make the information provided accessible to "researchers and students in Computer Science, Engineering, Psychology or Physics". This is a difficult set of goals; the extent to which they are met will be considered below.

The book is divided into four sections and an appen- dix. Part 1 provides the essential information that any good computer scientist should know about neural networks. For example, the author covers the funda- mentals of multilayer perceptrons and backpropagation networks; representation issues as well as the basic structure of connectionist models. He includes a number of programming projects to reinforce the ideas (an essential feature), as well as detailed examples. This is very well done, is not dry and is permeated with interesting commentary and remarks.

Part 2 is devoted to single-cell and single-layer models. This section comprises five chapters, and numerous example problems such as the Travelling Salesman problem. Again, programming projects are included for each of the techniques discussed. Both supervised and unsupervised approaches to learning are covered. Part 3 contains six chapters, and is di- rected at learning in multilayer models. This is an extremely comprehensive section, covering construc- tive algorithms that grow networks in the course of learning, with greater detail on backpropagation (and its variations and applications), as well as the basics of multilayer networks.

The fourth part introduces the concept of neural network expert systems. In particular, it describes the MACIE system (developed by the author, and now marketed as knowledgeNet). This section begins with a very brief description of heuristic rule-based expert systems, and presents the weaknesses of this approach. Unfortunately, this is the weakest section of this book. The description of a heuristic expert is extremely brief, to the point of being almost useless unless you already know a reasonable amount about the subject. Indeed,

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the section on the limitations of heuristic expert systems is almost derisively short.

The description of MACIE is enticing, but I found that it does not provide enough detail for me to really understand how it works; I got the gist, but not the detail. Essentially, a network is used to perform a classification task, then forward-chaining rules are extracted from the network. This is an interesting aspect of the system (covered in the final chapter).

Having described what the book is about, how good is it at achieving the goals stated earlier? The author certainly provides an excellent systematic development of neural network learning algorithms that would be suitable for most computer-literate, numerically aware, machine learning audiences. I particularly like his prac- tical approach to the development of the different algorithms, which includes numerous examples and a number of programming projects. Used as an introduc- tion to neural networks, this is an excellent book.

However, the title of the book is Neural Network Learning and Expert Systems, and the author's second stated goal is the presentation of neural network expert systems. The section on expert systems and hybrid neural network expert systems is indicative of the lack of work in this area. The result is that the author does not meet his second goal, even though he makes much of this aspect of the book in the foreword.

JOHN HUNT University of Wales, Aberystwyth

CAD Frameworks--Principles and Architecture, by Pieter van der Wolf. Kluwer Academic Publishers, The Netherlands (1994). 225pp. £66.75. ISBN: 0 7923 9501 8.

CAD systems are finding a wide range of applications in electronic engineering. When a variety of design and planning software can be linked in the same computer system, such systems call for CAD frameworks. These integration tools have an important role nowadays, and will continue to do so in the future. Consequently, this book deals with a very important topic. The book is intended in the first place for electronic design automa- tion professionals who are interested in the design and construction of CAD frameworks. The principles used in building integrated design environments, and the concepts involved, could also be useful to those