Priddy K.L., Keller P.E. Artificial Neural Networks. An Introduction

SPIE Press, 2005. — 181 p.This text introduces the reader to the fascinating world of artificial neural networks, a journey that the authors are here to help you with. The authors have written this book for the reader who wants to understand artificial neural networks without necessarily being bogged down in the mathematics. A glossary is included to assist the reader in understanding any unfamiliar terms. For those who desire the math, sufficient detail for most of the common neural network algorithms is included in the appendixes.
The concept of data-driven computing is the overriding principle upon which neural networks have been built. Many problems exist for which data are plentiful, but there is no underlying knowledge of the process that converts the measured inputs into the observed outputs. Artificial neural networks are well suited to this class of problem because they are excellent data mappers in that they map inputs to outputs. This text illustrates how this is done with examples and relevant snippets of theory.Learning Methods
Data Normalization
Data Collection, Preparation, Labeling, and Input Coding
Output Coding
Post-processing
Supervised Training Methods
Unsupervised Training Methods
Recurrent Neural Networks
A Plethora of Applications
Dealing with Limited Amounts of DataA: The Feedforward Neural Network
B: Feature Saliency
C: MatLAB Code for Various Neural Networks
D: Glossary of Terms