Dynamical Systems with Saturation Nonlinearities: Analysis and Design (Lecture Notes in Control and Information Sciences)

Derong Liu/ Anthony N. Michel

ISBN 10: 3540198881 / ISBN 13: 9783540198888

Editore: Springer, 1994

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212 pages. 9.30x6.15x0.48 inches. In Stock.

Codice articolo x-3540198881

Dati bibliografici

• Titolo: Dynamical Systems with Saturation Nonlinearities: Analysis and Design (Lecture Notes in Control and Information Sciences)
• Editore: Springer
• Data di pubblicazione: 1994
• Lingua: Inglese
• ISBN 10: 3540198881
• ISBN 13: 9783540198888
• Rilegatura: Paperback
• Condizione: Brand New
• Peso: 0,34 chilogrammi

Descrizione articolo

Riassunto

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.

Contenuti

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.