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.
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Technology
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
ISBN-13
978-0-444-89330-7 (9780444893307)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Klassifikation
Herausgeber*in
Universite Rene Descartes, Paris, France
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, &mgr;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).
