Abstract
The modelling and control for a class of SISO discrete-time nonlinear systems is discussed in this paper using multilayered feedforward neural networks (MFNNs). The ability of MFNNs to model arbitrary nonlinear functions is incorporated to approximate the unknown nonlinear I/O relationship and its inverse using a novel learning algorithm. In order to overcome the difficulties associated with simultaneous online identification and control in neural networks based control systems, the new learning control architectures which are based on the Kalman filter equations are developed for control systems with online identification and control ability. The simulation results are also provided. >
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.
