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Knowledge-Based Neurocomputing

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ISBN-10: 0262032740

ISBN-13: 9780262032742

Edition: 1999

Authors: Ian Cloete, Jacek M. Zurada

List price: $14.75
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Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network. The key assumption of knowledge-based neurocomputing…    
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Book details

List price: $14.75
Copyright year: 1999
Publisher: MIT Press
Publication date: 2/4/2000
Binding: Hardcover
Pages: 502
Size: 8.25" wide x 10.00" long x 1.25" tall
Weight: 2.992
Language: English

Preface and Acknowledgements
Knowledge-Base Neurocomputing: Past, Present, and Future
Architectures and Techniques for Knowledge-Based Neurocomputing
Symbolic Knowledge Representation in Recurrent Neural Networks: Insights from Theoretical Models of Computation
A Tutorial on Neurocomputing of Structures
Structural Learning and Rule Discovery
VL1ANN: Transformation of Rules to Artificial Neural Networks
Integration of Heterogeneous Sources of Partial Domain Knowledge
Approximation of Differential Equations Using Neural Networks
Fynesse: A Hybrid Architecture for Self-Learning Control
Data Mining Techniques for Designing Neural Network Time Series Predictors
Extraction of Decision Trees from Artificial Neural Networks
Extraction of Linguistic Rules from Data via Neural Networks and Fuzzy Approximation
Neural Knowledge Processing in Expert Systems