| 模糊遗传滚动优化的LS-SVM预测控制研究 |
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| 引用本文： | 高异,杨延西,刘军.模糊遗传滚动优化的LS-SVM预测控制研究[J].系统仿真学报,2007,19(6):1277-1280. |
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| 作者姓名： | 高异 杨延西 刘军 |
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| 作者单位： | 西安理工大学107#,西安,710048 |
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| 摘 要： | 提出一种基于最小二乘支持向量机(LS-SVM)的非线性系统预测控制算法。该算法通过LS-SVM对非线性系统输入输出数据序列的训练学习建立其预测模型;基于模糊遗传算法完成非线性预测控制的滚动优化过程。仿真结果表明基于该方法的非线性系统预测控制比基于RBF神经网络的预测控制具有更好的控制效果。
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| 关 键 词： | 预测控制 最小二乘支持向量机 模糊遗传算法 神经网络 |
| 文章编号： | 1004-731X（2007）06-1277-04 |
| 收稿时间： | 2006-02-07 |
| 修稿时间： | 2006-12-28 |
Research on LS-SVM Predictive Control Using Fuzzy Genetic Algorithm Rolling Optimization |
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| GAO Yi,YANG Yan-xi,LIU Jun.Research on LS-SVM Predictive Control Using Fuzzy Genetic Algorithm Rolling Optimization[J].Journal of System Simulation,2007,19(6):1277-1280. |
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| Authors: | GAO Yi YANG Yan-xi LIU Jun |
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| Abstract: | A nonlinear predictive control algorithm based on least squares support vector machines (LS-SVM) model was proposed. The nonlinear off-line model of the nonlinear system was obtained by LS-SVM to train a sequence data of input and output. The whole rolling optimization procedure was finished by fuzzy genetic algorithms (FGA). The simulation results illustrate that the nonlinear predictive control using LS-SVM is more effective than using RBF network. |
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| Keywords: | predictive control least square support vector machines fuzzy genetic algorithms neural network |
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