Information-Theoretic Aspects of Neural Networks

Features

• Cover Type: Hard Cover with 416 pages
• Published by: CRC
• Edition: 1st Edition March 30, 1999
• Written in: English
• ISBN 10 Number: 0849331986
• ISBN 13 Number: 978-0849331985
• Book Dimensions: 9.6 x 6.4 x 1 inches
• Weighs: 1.5 pounds

Product Description
Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information. Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as: · Shannon information and information dynamics · neural complexity as an information processing system · memory and information storage in the interconnected neural web · extremum (maximum and minimum) information entropy · neural network training · non-conventional, statistical distance-measures for neural network optimizations · symmetric and asymmetric characteristics of information-theoretic error-metrics · algorithmic complexity based representation of neural information-theoretic parameters · genetic algorithms versus neural information · dynamics of neurocybernetics viewed in the information-theoretic plane · nonlinear, information-theoretic transfer function of the neural cellular units · statistical mechanics, neural networks, and information theory · semiotic framework of neural information processing and neural information flow · fuzzy information and neural networks · neural dynamics conceived through fuzzy information parameters · neural information flow dynamics · informatics of neural stochastic resonance Information-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.

Book Info
A resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain.

Reader Reviews
This book has dealt with in depth the various entropy error measures that can be used for the neural networks. The Minimum entropy error measures and the Maximum entropy error functions like the Kapur measures, Sharma-Mittal error measures are discussed as an alternative to the classical square error methods when the input and the teacher values are stochastical variables. The book also has good introduction and is well written. The book has experimental data to support the claims made by the author.