Parameter Identification for Wiener Model Using Particle Swarm Optimization with a Case Study

Abstract

For a class of nonlinear systems described by Wiener model, the model parameter identification problem is equivalent to the nonlinear minimization problem with the estimated parameters as the optimized variables subjected to some equality and inequality constraints. Then the particle swarm optimization (PSO) algorithm is used to obtain the optimal solution to the minimization problem (i.e. the optimal estimation of Wiener model parameters) by searching in the whole parameter space. The inertia weight and learning gains in PSO algorithm are modified through analyzing particle trajectory. A numeric simulation of a Wiener model is provided to verify the effectiveness of the proposed identification scheme. Finally, PSO based parameter identification method is applied to the quality model for a continuous annealing furnace, achieving some satisfactory identification results.