摘要
将重要抽样(IS)法与神经网络(NN)用于不确定控制系统的模糊鲁棒性分析中.IS法被用于提高当模糊不可接受性能的概率很小时的抽样效率,而NN被用于预测每次仿真试验中所需计算时间较长的性能指标值.所建议方法降低了标准MonteCarlo仿真(MCS)方法在处理模糊鲁棒性分析中小概率事件以及性能指标计算时间较长所带来的过高计算成本.最后,仿真结果验证了方法的有效性.
This paper applies the importance sampling (IS) method and neural network (NN) to the fuzzy robustness analysis of uncertain control systems.The IS method is utilized to improve the sampling efficiency when the probability of fuzzy unacceptable performance is very small.The NN is used to predict the performance index requiring more computational time in each simulation experiment.The proposed approach can reduce the excessive computational cost generated from the standard Monte Carlo simulation (MCS) for dealing with the rare event case and the performance index requiring more computational time in the fuzzy robustness analysis.Finally,a numerical example is provided to demonstrate the effectiveness of the proposed method.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2005年第2期335-340,共6页
Control Theory & Applications
基金
国家自然科学(青年)基金资助项目(60204011)
国家自然科学基金资助项目(60274057).
