Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Abstract

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.

Fitting of TC model according to key parameters affecting Parkinson's state based on improved particle swarm optimization algorithm

“…These algorithms are local optimization algorithms, that is, the search effect depends on the initial values of the parameters. In recent years, some evolutionary algorithms have been introduced into the parameter fitting process, such as genetic algorithm 19 , differential evolution algorithm 20 and particle swarm optimization algorithm 21 . These algorithms optimize the optimal solution through multi-dimensional parallel work.…”