A joint frequency-position domain structure identification of nonlinear discrete-time systems by neural networks

A new technique is proposed to identify the structure and 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 GBF is also presented, where the spatial Gaussian envelope of GBF is replaced with a triangular one. A modification to the GBF has also been introduced in order to suppress the effects of noise on the procedure. A three-layered neural network, augmented with nonuniform sampling, is described for solving the system identification problem.<
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