Model structure selection strategy for Wiener model identification with piecewise linearisation

This paper presents a method for identifying the optimum structure of Wiener model with pieeewise linearisation. The number of pieeewise linear functions for estimating the static nonlinear and the maximum lag of the linear dynamic part of the Wiener model are selected by cross-validation based approach. The maximum lag and the number of partitions are selected in two subsequence steps. Three popular model selection criteria, i.e. FPE, PRESS, and CP, are considered and compared in the selection process. With the ultimate aim of compensation for nonlinearities in sensors, we have illustrated the feasibility of using the proposed method to compensate hard nonlinearities, such as discontinuous nonlinear and saturation. The results from this work can be used as a guideline of model selection for Wiener model identification and nonlinear compensations of sensor.