Nonlinear time-variant systems identification based on neural networks combined with basis sequence approximation

This paper presents a new method for identifying nonlinear time-variant systems with unknown structure. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non-linearity of the system, characterize time- invariant dynamics of the system by the time-invariant parametric vector of the network, and then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time-variant systems with unknown structure. In order to improve the real-time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.