Particle Swarm Optimization Combined with Molecular Force and Its Application for Parameter Estimation

Parameter estimation is the critical part of system identification and regression analysis and it relates to the application and promotion of nonlinear model. The parameter estimation problem of nonlinear model is transformed into an unconstrained multi-dimensional function optimization problem. The particle swarm optimization algorithm based on the molecular force (MPSO), which is enlightened by molecular kinetic theory, is used to solve this problem, just taking the asymptotic regression model for example which is widespread in natural sciences and social sciences. There are real data and random sample data in the experiments. The random sample data is applied to analyze the impact of dimensions of parameter estimation and sampling interval on the algorithm performance, and experimental results show that MPSO algorithm is an effective nonlinear model parameter estimation method.