Improving strategies on fuzzy neural network control for nonlinear object
非线性系统模糊神经网络控制的改进策略

Based on the conventional control methods, we study and improve the fuzzy-neural-network-adaptive control for a system with unknown nonlinearities. The controller, identifier and optimization algorithm of the scheme are designed respectively by the improved methods. A structure-improved PID--type fuzzy-neural-network is used as the controller, and the least squares support-vector-machine(LS--SVM) is employed as the identifier. The parameters of the controller are optimized by the offline quantum-behaved particle-swarm-optimization(QPSO) with chaos strategy combined with the online-error-back-propagation tuning. The kernel parameters of the LS--SVM are optimized by PSO with chaos optimization. The stability of the improved scheme is discussed in the conclusion section to complete the presentation of the whole design method. Finally, simulation results on a heat exchanger show the feasibility and validity of the designed control system.