Parameter estimation of multivariable Wiener nonlinear systems by the improved particle swarm optimization and coupling identification

This paper investigates the parameter estimation of multivariable Wiener nonlinear systems. To solve the inconsistency problem of the parameter vector and the parameter matrix, the coupling identification concept is applied. Combined with particle swarm optimization (PSO) and an auxiliary model, the partially coupled improved particle swarm optimization (PC -IPSO) method is proposed. In this algorithm, the adaptive feedback inertia weight is improved to accelerate the convergence speed, and the retirement update mechanism is introduced to improve the optimization ability of the basic PSO algorithm. To verify the performance of PC -IPSO, we also derive a multivariable improved PSO (M -IPSO) method for comparison. The computational complexity analysis shows that the PC -IPSO algorithm requires less computational resources than the M -IPSO algorithm. Then, the convergence of the improved PSO method is analyzed. The simulation results indicate that the PC -IPSO method has a faster convergence speed and higher identification accuracy than the M -IPSO and several existing state-of-the-art methods for multivariable Wiener system identification.