Weighted differential evolution-based heuristic computing for identification of Hammerstein systems in electrically stimulated muscle modeling

초록

In this paper, weighted differential evolution (WDE)-based evolutionary heuristic computing is presented for parameter estimation of nonlinear dynamics of the electrically stimulated muscle system based on a Hammerstein model structure as an element of rehabilitation program for the stroke patient. The approximation theory is defined on the basis of mean square error of actual and estimated responses for the system's fitness function. Optimization technique based on WDE is employed to find the optimal parameters of ESM model with sigmoidal, polynomial and spline as input nonlinear functions. Statistical analysis verified the worth of WDE as computational heuristic technique for identification of electrically stimulated muscle system.

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