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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
