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.
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Technology
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Illustrationen
Maße
ISBN-13
978-0-444-88533-3 (9780444885333)
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Schweitzer Klassifikation
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).