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. In this paper the proposed delayed-input delayed-state network architecture and the general training scheme are described. A solution for the problem of analog signals representation by a binary neural network is suggested. Illustrative simulation results are promising and show good and robust performance in various cases.
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