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GENERAL INFORMATION
Artificial Neural Networks
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Description
This tutorial text provides the reader with an understanding of artificial neural networks (ANNs) and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed and the data collection processes, to the many ways ANNs are being used today.
The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.
Keywords: neural networks, ANN, artificial neurons, data-driven, algorithms, biologically inspired, neural network, associative memory
Table of Contents
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Front Matter
Open Access
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1. Introduction
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2. Learning Methods
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3. Data Normalization
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5. Output Coding
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6. Post-processing
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7. Supervised Training Methods
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8. Unsupervised Training Methods
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9. Recurrent Neural Networks
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10. A Plethora of Applications
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B. Feature Saliency
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D. Glossary of Terms
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Back Matter
Open Access
Excerpt
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.
The authors have enjoyed writing the text and welcome readers to dig further and learn how artificial neural networks are changing the world around them.
©2005 Society of Photo-Optical Instrumentation Engineers
SPIE Digital Library
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