Abstract
Nonlinear systems identification techniques have been used to study the stretch reflex dynamics of the muscles of the human ankle. Kearney and Hunter (1988) suggested a parallel cascade structure consisting of two pathways to represent human ankle stiffness dynamics. The upper pathway contains a linear model that represents the intrinsic stiffness of the ankle while the reflex stiffness, the lower path, is modeled with a Hammerstein cascade. In this work, an algorithm for the identification of the parallel cascade model using SVM regression is developed. The model parameters have been identified by minimizing an epsilon insensitive cost functions based on either the sum of absolute residuals or the sum of square residuals. The proposed algorithm has been validated via simulation.
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