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中文摘要：
针对协同微粒群算法不能保证收敛到局部或全局最优值的问题，提出一种改进协同微粒群算法（ICPSO），并证明了该算法能以概率1收敛于全局最优解．应用ICPSO建立一类非线性对象的神经网络辨识模型，并对系统的模糊神经网络自适应控制器的参数进行了离线和在线优化．仿真结果表明，ICPSO能提高系统的建模精度，增强模型的泛化能力，而且由ICPSO训练的控制器可以达到良好的控制效果．
英文摘要：
An improved cooperative PSO （ICPSO） is proposed for the cooperative PSO＇ incapable of converging at the local or global optimum. It is proved that the algorithm can converge at the global optimization solution with probability one. ICPSO is applied to the neural network modeling of a nonlinear plant, and also employed to the offline and online training of the fuzzy neural network adaptive controller in the system. Simulation results show that ICPSO has advantages of increasing the precision and enhancing the generalization capability of the model, and the fuzzy neural network（FNN） controller trained by ICPSO is effective for the system control.
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