Application of Online System Identification Based on Improved Quantum-Behaved Particle Swarm Optimization

Application of online system identification based on improved quantum-behaved particle swarm optimization is studied in this paper. QPSO algorithm combined with the single neuron can improve the local search capabilities and identification accuracy. Then the improved QPSO is applied to online identify parameters of a system described by differential equations and compared with the improved particle swarm algorithm and genetic algorithm. Simulation results show that improved QPSO algorithm significantly accelerates the online identification. Moreover the convergence speed and accuracy of the improved QPSO is far better than that of the improved PSO and GA algorithm. Time-delay and parameter changes for the simulation experiment illustrates the stability and tracking capability of improved QPSO algorithm are better than the improved PSO algorithm.