The artificial neural network book

This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.

The Book of GENESIS - Exploring Realistic Neural Models with the GEneral NEural SImulation System is the first publication to thoroughly and accurately describe how to use the "GENESIS" simulation/modeling software system available through the Internet file-server at the California Institute of Technology, Pasadena, CA., Department of Computation and Neural Systems.
The first part of the book consists of edited contributions from an international team of neural networks researchers working with "GENESIS" which show the reader/user the kind of models/simulations which can be created by using the software. The second part is a step-by-step tutorial for all professionals, researchers and students working in the area of neural networks and the cognitive sciences. The material has been extensively class tested at Cal Tech and Woods Hole Oceanographic Labs, Massachusetts and shows the student/user how to effectively manipulate the software and use the material to one's best advantage.

The book contains black-and-white illustrations.

This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

Freeman and Skapura provide a practical introduction to artificial neural systems (ANS). The authors survey the most common neural-network architectures and show how neural networks can be used to solve actual scientific and engineering problems and describe methodologies for simulating neural-network architectures on traditional digital computing systems. A textbook for a graduate or advanced undergraduate course in neural networks for computer-science or engineering students. Presumes the standard calculus, differential equations, and advanced mathematics acquired in the first three years of an engineering curriculum. Includes models inspired by, but not found by, studies of the brain. Annotation c Explains how to use the GENESIS simulation/modelling software system available through the Internet file-server at the California Institute of Technology, Pasadena, USA. It includes a step-by-step tutorial which should benefit those researching neural networks and the cognitive sciences. This is a comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions