| 基于神经网络模型和改进最优保留遗传算法的非线性系统多步预测控制 |
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| 引用本文： | 陈红艳,李平,刘桂芝,李海阳.基于神经网络模型和改进最优保留遗传算法的非线性系统多步预测控制[J].河北省科学院学报,2002,19(4):198-203. |
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| 作者姓名： | 陈红艳 李平 刘桂芝 李海阳 |
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| 作者单位： | 辽宁石油化工大学信息工程分院,辽宁,抚顺,113001 |
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| 摘 要： | 针对复杂工业过程中存在的时滞、慢时变、强干扰等非线性控制对象提出一种利用径向基函数神经网络 (RBFNN)作为预测模型 ,改进最优保留遗传算法 (MEGA)作为滚动优化策略的非线性预测控制算法。仿真结果表明该算法具有较强的鲁棒性和抗时变性能。
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| 关 键 词： | 改进最优保留遗传算法(MEGA) 径向基函数神经网络(RBFNN) 非线性预测控制 鲁棒性 时滞 复杂工业过程 |
| 文章编号： | 1001-9383(2002)04-0198-06 |
| 修稿时间： | 2002年7月16日 |
Nonlinear system multi-step predictive control based neural network model and modified elitist preserved genetic algorithm |
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| CHEN Hong-yan,LI Ping,LIU Gui-zhi,LI Hai-yang.Nonlinear system multi-step predictive control based neural network model and modified elitist preserved genetic algorithm[J].Journal of The Hebei Academy of Sciences,2002,19(4):198-203. |
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| Authors: | CHEN Hong-yan LI Ping LIU Gui-zhi LI Hai-yang |
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| Abstract: | The paper proposes a nonlinear multi-step predictive control strategy using Radial Basis Function Neural Network (RBFNN) as multi-step predictive model for nonlinear complicated industrialized process with time delay, slow time variety and highly disturbance. The modified elitist preserved genetic algorithm is used to obtain the online nonlinear optimization. Simulation results demonstrate that the strategy has good robustness and the resisting time variety ability. |
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| Keywords: | Modified preserved elitist genetic algorithm (MEGA) Radial Basis Function Neural Network(RBFNN) Nonlinear predictive control Robustness Time delay Complicated industrialized process |
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