Design of meta-heuristic computing paradigms for Hammerstein identification systems in electrically stimulated muscle models

In this study, a novel application of differential evolution (DE)-based computational heuristics is proposed for the identification of Hammerstein structures representing the electrically stimulated muscle (ESM) models as a part of rehabilitation interventions for the stock patient to prevent the post-spinal cord injury atrophy. The strength of approximation theory is incorporated for defining the fitness function for ESM system based on mean square deviation between actual and estimated responses. DE, genetic algorithms (GAs), particle swarm optimization (PSO), pattern search (PS), and simulated annealing (SA) are used as optimization mechanisms to identify the ESM models with input nonlinearities of sigmoidal, polynomial, and spline kernels for noiseless and noisy environments. Comparative studies based on detailed statistics establish the worth of DE-based heuristics over its counterparts GAs, PSO, PS, and SA in terms of accuracy, convergence, robustness, and efficiency for the identification of ESM models arising in rehabilitation of the stock patients.