Identification of NARX Hammerstein Models Based on Support Vector Machines
نویسندگان
چکیده
This paper presents a new algorithm for identification of NARX Hammerstein systems using support vector machines (SVMs) to model the static nonlinear elements. The SVM is fitted by minimizing an ε-insensitive, L-1 cost function which is robust in the presence of outliers. Another advantage of this algorithm is that the value of the uncertainty level epsilon can be specified by the user which gives more control on the sparseness of the solution. The effect of this choice is demonstrated using simulations.
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