Neural network architecture for adaptive system modeling and control

Author

Levin, Esther ; Gewirtzman, Raanan ; Inbar, G.F.

Author_Institution

Dept. of Electr. Eng., Technion, Haifa, Israel

fYear

1989

fDate

0-0 1989

Firstpage

311

Abstract

A computing architecture for adaptive control and system modeling based on computational features of nonlinear discrete neural networks is proposed. These features are massively parallel and distributed structures for signal processing, with the potential for ever-improving performance through dynamical learning. The proposed delayed-input delayed-state network architecture and the general training scheme are described. A solution for the problem of analog signal representation by a binary neural network is suggested. Illustrative simulation results are promising and show good and robust performance in various cases.<>

Keywords

adaptive control; adaptive systems; learning systems; neural nets; parallel architectures; virtual machines; adaptive control; adaptive system modeling; analog signal representation; binary neural network; computing architecture; delayed-input delayed-state network architecture; distributed structures; dynamical learning; learning systems; nonlinear discrete neural networks; parallel architectures; signal processing; training scheme; Adaptive control; Adaptive systems; Learning systems; Neural networks; Parallel architectures; Virtual computers;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1989. IJCNN., International Joint Conference on

Conference_Location

Washington, DC, USA

Type

conf

DOI

10.1109/IJCNN.1989.118716

Filename

118716