Dynamical Systems with Saturation Nonlinearities
Analysis and Design
Published on 28. July 1994
Book
Paperback/Softback
XIV, 197 pages
978-3-540-19888-8 (ISBN)
Description
This three-part monograph addresses topics in the areas of control systems, signal processing and neural networks. Procedures and results are determined which constitute the first successful synthesis procedure for associative memories by means of artificial neural networks with arbitrarily pre-specified full or partial interconnecting structure and with or without symmetry constraints for the connection matrix.
More details
Series
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XIV, 197 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 12 mm
Weight
330 gr
ISBN-13
978-3-540-19888-8 (9783540198888)
DOI
10.1007/BFb0032146
Schweitzer Classification
Persons
Anthony N. Michel, PhD, is Professor Emeritus in the College of Engineering at the University of Notre Dame.
Ling Hou, PhD, is Professor in the Department of Electrical and Computer Engineering at St. Cloud State University.
Derong Liu, PhD, is Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago.
Content
to dynamical systems with saturation nonlinearities.- Qualitative theory of control systems with control constraints and state saturation: Two fundamental issues.- Asymptotic stability of dynamical systems with state saturation.- Null controllability of discrete-time dynamical systems with control constraints and state saturation.- Stability analysis of one-dimensional and multidimesional state-space digital filters with overflow nonlinearities.- Criteria for the absence of overflow oscillations in fixed-point digital filters using generalized overflow characteristics.- Stability analysis of state-space realizations for multidimensional filters with overflow nonlinearities.- to part III.- Analysis and synthesis of a class of neural networks with piecewise linear saturation activation functions.- Sparsely interconnected neural networks for associative memories with applications to cellular neural networks.- Robustness analysis of a class sparsely interconnected neural networks with applications to design problem.