Abstract
A state space representation of both continuous and discrete time mathematical models of Recurrent Neural Networks (RNN) are given in two layer Jordan canonical architecture, and a new improved Back-Propagation (BP) type learning method, is proposed. Some topology improvements, are suggested. The proposed RTNN model is linear in small and nonlinear in large, which permits to apply all well known state- and output linear systems design methods. The obtained RTNN model is incorporated in a rule-based fuzzy system, giving the possibility to approximate and to identify a complex nonlinear plants. Simulation results of nonlinear systems identification by the proposed fuzzy-neural system, using RNN BP learning, are given.
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.
