Neural networks : advances and applications
Author(s)
Bibliographic Information
Neural networks : advances and applications
North-Holland, 1991-
- [1]
- 2
Available at 52 libraries
Note
Includes bibliographical references
Description and Table of Contents
- Volume
-
[1] ISBN 9780444885333
Description
It is expected that Neural Networks will find their niche among the methods and techniques that computer scientists use for intrinsically difficult problems. An attraction of Neural Networks is the dialogue established between computer science, biology, physics, psychology, numerical and non-linear analysis, and other areas. In the future, it may be discovered that Neural Networks are useful for the computationally fast and approximate solution of certain decision problems which are based on simultaneously acting diverse criteria with information of different forms. This book is a snapshot of academic and industrial research in Neural Network theory and of its major applications, written by active contributors to the field, including computer scientists, electrical engineers and physicists.
Table of Contents
Theory of the Random Neural Network (E. Gelenbe). Abstraction Hierarchy in Neural Networks: A Rigorous Treatment (R. Hong Tuan). Recent Applications of Competitive Activation Mechanisms (J.A. Reggia, Y. Peng, P. Bourret). Improving the Learning Speed in Topological Maps of Patterns (J.S. Rodrigues, L.B. Almeida). Performance of Higher Order Neural Networks in Invariant Recognition (S. Kollias, A. Stafylopatis, A. Tirakis). Visual Recognition of Script Characters and Neural Network Architectures (J. Skrzypek, J. Hoffman). A Distributed Decorrelation Algorithm (F.M. Silva, L.B. Almeida). Neural Networks and Combinatorial Optimization: A Study of NP-Complete Graph Problems (L. Herault, J.-J. Niez). Merging Multilayer Perceptrons and Hidden Markov Models: Some Experiments in Continuous Speech Recognition (H. Bourlard, N. Morgan). Introduction to Neural Networks and their Application to Process Control (E. Tulunay).
- Volume
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2 ISBN 9780444893307
Description
The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously. As the title indicates, it combines the presentation of recent methodological results concerning computational models and results inspired by neural networks, and of well-documented applications which illustrate the use of such models in the solution of difficult problems. The volume is balanced with respect to these two orientations: it contains six papers concerning methodological developments and five papers concerning applications and examples illustrating the theoretical developments. Each paper is largely self-contained and includes a complete bibliography.The methodological part of the book contains two papers on learning, one paper which presents a computational model of intracortical inhibitory effects, a paper presenting a new development of the random neural network, and two papers on associative memory models. The applications and examples portion contains papers on image compression, associative recall of simple typed images, learning applied to typed images, stereo disparity detection, and combinatorial optimisation.
Table of Contents
Learning in the Recurrent Random Neural Network (E. Gelenbe). Generalization Performance of Feed-Forward Neural Networks (S. Shekhar et al.). The Nature of Intracortical Inhibitory Effects (J.A. Reggia et al.). Random Neural Networks with Multiple Classes of Signals (J.-M. Fourneau, E. Gelenbe). The MicroCircuit Associative Memory, AM: A Biologically Motivated Memory Architecture (C.F. Miles, D. Rogers). Generalised Associative Memory and the Computation of Membership Functions (E. Gelenbe). Layered Neural Network for Stereo Disparity Detection (E. Maeda et al.). Storage and Recognition Methods for the Random Neural Network (M. Mokhtari). Neural Networks for Image Compression (S. Carrato). Autoassociative Memory with the Random Neural Network using Gelenbe's Learning Algorithm (C. Hubert). Minimum Graph Covering with the Random Neural Network Model (E. Gelenbe, F. Batty).
by "Nielsen BookData"