Modeling of Nonlinear Time-varying Systems Using Time-varying Neural Networks
A unified architecture of time-varying neural networks is presented for implementing unknown nonlinear time-varying mappings and modeling nonlinear time-varying systems.The weight training for the neural networks is taken as the time-varying parameters estimation of time-varying systems.Applying the iterative learning mechanism,this paper develops an iterative learning least squares algorithm for updating the weights along the iteration axis.The global convergence is established theoretically,and two numerical examples are presented to demonstrate effectiveness of the proposed learning algorithms for modeling nonlinear time-varying systems.