PRINCIPLES OF ARTIFICIAL NEURAL NETWORKS

by Wai-Kai Chen

This textbook is intended for a first-year graduate course on Artificial Neural Networks. It assumes no prior background in the subject and is directed to MS students in electrical engineering, computer science and related fields, with background in at least one programming language or in a programming tool such as Matlab, and who have taken the basic undergraduate classes in systems or in signal processing.The uniqueness of the book is in the breadth of its coverage over the range of all major artificial neural network approaches and in extensive hands-on case-studies on each and every neural network considered. These detailed case studies include complete program print-outs and results and deal with a range of problems, to illustrate the reader's ability to solve problems ranging from speech recognition, character recognition to control and signal processing problems, all on the basis of following the present text. Another unique aspect of the text is its coverage of important new topics of recurrent (time-cycling) networks and of large memory storage and retrieval problems.The text also attempts to show the reader how he can modify or combine one or more of the neural networks covered, to tailor them to a given problem which does not appear to fit any of the more standard designs, as is very often the case.

252 pages, Paperback

First published July 15, 1997

Reviews

[Mike · Rating 4 out of 5]

This is a challenging introduction to artificial neural networks by using a number of networks through history as case studies. I used this book as a companion for a course taught by the author. The course itself was excellent, and very challenging. The book contained invaluable examples, however the descriptions were fairly brief. It is interesting in the history of the evolution of neural networks, and to use as a reference. Compared to current knowledge and use of neural networks, this is somewhat out of date, but if you need an introduction into the theory behind all neural networks and machine learning, this could be a good entry point.

[Izhan Noorzi · Rating 3 out of 5]

Taking into consideration, this is a basic book for neuroscience scholars.