Identification for nonlinear system described by functional expansion with noisy input and output.

概要

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We propose the method to determine the system structure and to estimate the parameters for the nonlinear system, described by the functional expansion.First, we hypothesize candidate models of different structure. We may estimate the system parameters of each model by the proposed criterion function which is based on the maximum likelihood method. The estimated parameters are obtained analytically from noisy input and output data, and they are consistent.Secondly, we calculate a posteriori probability for each model by using Bayesian theorem. We adopt the model whose a posteriori probability is maximum as the optimal one.Finally, we verify the validity of this method by digital simulations.
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