Title :
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
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;
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
DOI :
10.1109/IJCNN.1989.118716
