Abstract
A parametric recurrent neural network model and an improved dynamic backpropagation method of its learning, are applied for nonlinear plants identification and state estimation. The obtained parameters of the RNN model are used for design of an indirect adaptive control system. The paper suggests three main types of state-space control with RNN state estimation: a proportional; a proportional plus integral and a trajectory-tracking control. The applicability of the proposed neural indirect adaptive control schemes is confirmed by simulation results.
Sign-in/Register to access full text options 
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.
