| 引用本文: | 吴淮宁, 李 勇, 蔡开元.基于重要抽样法和神经网络的模糊鲁棒性分析[J].控制理论与应用,2005,22(2):335~340.[点击复制] |
| WU Huai-ning, LI Yong, CAI Kai-yuan.Fuzzy robustness analysis based on importance sampling and neural network[J].Control Theory and Technology,2005,22(2):335~340.[点击复制] |
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| 基于重要抽样法和神经网络的模糊鲁棒性分析 |
| Fuzzy robustness analysis based on importance sampling and neural network |
| 摘要点击 1690 全文点击 1734 投稿时间：2003-10-21 修订日期：2004-04-09 |
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| DOI编号 10.7641/j.issn.1000-8152.2005.2.033 |
| 2005,22(2):335-340 |
| 中文关键词 不确定控制系统 鲁棒性分析 模糊方法 神经网络 重要抽样 MonteCarlo仿真 |
| 英文关键词 uncertain control systems robustness analysis fuzzy approach neural network(NN) importance sampling Monte Carlo simulation |
| 基金项目 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). |
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| 中文摘要 |
| 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. |
| 英文摘要 |
| 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. |