Parameter Identification of Nonlinear Systems Model Based on Improved Differential Evolution Algorithm

Abstract

Aiming at the difficulty of optimizing the parameter estimation of nonlinear models, a new method for parameter identification of nonlinear system models based on improved differential evolution algorithm based on diversity evaluation index is proposed. By establishing a population reconstruction mechanism, based on the population diversity index, the concept of population similarity is proposed to guide the selection of evolutionary strategies and the adaptive adjustment of process parameters, thereby balancing the global and local search functions at different stages. The population size decreasing strategy effectively reduces the amount of computation and improves the convergence speed and algorithm efficiency. In order to verify the performance of the algorithm, several types of standard functions with typical complex mathematical characteristics are simulated and applied to the identification of a type of thermal system model parameters. The results show that the improved algorithm has high model parameter identification accuracy and faster convergence speed, which effectively improves the accuracy and efficiency of model establishment, and provides a feasible way to solve the model parameter identification problem in practical systems.