Abstract

Methods for the identification of Hammerstein models consisting of a Support Vector Machine nonlinearity followed by an ARX linear system are developed. The models are identified by minimizing epsilon insensitive cost functions based on either the sum of absolute residuals or the sum of squared residuals. Large scale implementations of these techniques are then derived using subset selection methods, and used to identify a model of the stretch reflex electromyogram from experimental data. The effects of the various cost functions and tuning parameters are demonstrated with the experimental results.

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