Structure recognition of nonlinear discrete-time systems by neural networks

Abstract

A technique is proposed to identify the structure as well as the parameters of nonlinear discrete-time system models. The structure is represented in a frequency-position domain of Gabor basis functions (GBFs). A simplification to the GBFs is also presented, where the spatial Gaussian envelope of GBFs is replaced with a triangular one. A modification to the GBFs has also been introduced in order to suppress noise effects on the procedure. A three-layered neural network, augmented with nonuniform sampling, is described for solving the system identification problem.<>