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Abstract

The computerized method for identifying Hammerstein models is a method in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN). Accurate identification of a Hammerstein model requires that output error between the actual and estimated systems be minimized. Thus, the problem of identification is an optimization problem. A hybrid algorithm, based on least mean square (LMS) principles and the Subspace Identification Method (SIM) is developed for the identification of the Hammerstein model. LMS is a gradient-based optimization algorithm that searches for optimal solutions in the negative direction of the gradient of a cost index. In the method, LMS is used for estimating the parameters of the RBFNN. For estimation of state-space matrices, the N4SID algorithm for subspace identification is used.

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