Backtracking search heuristics for identification of electrical muscle stimulation models using Hammerstein structure

The electrical muscle stimulation models (EMSMs) are effectively described through Hammerstein structure and are used to restore the functionality of paralyzed muscles after spinal cord injury (SCI). In the present study, global search efficacy of evolutionary computing paradigm through backtracking search algorithm (BSA) is exploited for parameter estimation of EMSMs. The approximation theory in mean squared error sense is used for the construction of a merit function for EMSMs based on deviation between optimal and approximated parameters. Variants of BSA are designed based on memory size and population dynamics for the minimization problem of EMSMs having cubic spline as well as sigmoidal nonlinearities. Comparative studies by means of rigorous statistics establish the worth of scheme for effective, accurate, reliable, robust and stable identification of EMSMs in rehabilitation scenarios of SCI. (C) 2019 Elsevier B.V. All rights reserved.