Identification of discrete-time varying nonlinear systems using time-varying neural networks

Iterative learning identification algorithms for time-varying neural networks training are presented, by which neural networks based identification for discrete-time varying nonlinear systems can be carried out, as the system undertaken performs tasks repeatedly over a finite time interval. This paper develops the iterative learning least squares algorithm with dead-zone for the weights updating along the iteration axis. In order to improve the convergence rate, the learning algorithm is modified by re-adjusting the covariance matrix. The proposed algorithms guarantee that the estimation error converges to a bound point wisely over the entire time interval. Numerical results are presented to demonstrate effectiveness of the proposed learning algorithms.