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Neural networks for intelligent signal processing /

This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their...

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Bibliographic Details
Main Authors: Zaknich, Anthony
Corporate Authors: World Scientific Firm
Published: World Scientific Pub. Co.,
Publisher Address: Singapore ; River Edge, N.J. :
Publication Dates: 2003.
Literature type: eBook
Language: English
Series: Series on innovative intelligence ; vol. 4
Subjects:
Neural networks Computer science
Signal processing > Digital techniques
Intelligent control systems
Electronic books
Online Access: http://www.worldscientific.com/worldscibooks/10.1142/5220#t=toc
Summary: This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects. The text also presents a comprehensive treatment of a class of neural networks called common bandwidth spherical basis function NNs, including the probabilistic NN, the modified probabilistic NN and the general regression NN.
Carrier Form: 1 online resource (xxiv,484pages) : illustrations.
Bibliography: Includes bibliographical references and index.
ISBN: 9789812796851 (electronic bk.)
CLC: TN911.7
Contents: 1. Introduction. 1.1. Motivation for ANNs. 1.2. ANN definitions and main types. 1.3. Specific ANN models. 1.4. ANN black box model. 1.5. ANN implementation. 1.6. When to use an ANN. 1.7. How to use an ANN. 1.8. General applications. 1.9. Pattern recognition examples. 1.10. Function mapping and filtering examples. 1.11. Motor control example. 1.12. ANN summary -- 2. A brief historical overview. 2.1. ANN history to 1970. 2.2. ANN history after 1970. 2.3. Reasons for the resurgence of interest in ANNs. 2.4. Historical summary -- 3. Basic concepts. 3.1. The basic model of the neuron. 3.2. Activa
10. Advanced MPNN developments. 10.1. A tuneable approximate piecewise linear model. 10.2. Integrated sensory intelligent system (ISIS). 10.3. Future directions for the MPNN -- 11. Neural networks similar to the common bandwidth spherical basis function regression ANNs. 11.1. Radial basis function neural network. 11.2. Cerebellar model articulation controller -- 12. Unsupervised learning neural networks. 12.1. Kohonen's self-organising map. 12.2. Adaptive resonance theory -- 13. Other neural network models. 13.1. Hopfield neural network. 13.2. Boltzmann machine. 13.3. Bidirectional associati

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