| 基于改进型RBF神经网络辨识的PID控制 |
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| 引用本文： | 李绍铭,刘寅虎. 基于改进型RBF神经网络辨识的PID控制[J]. 自动化与仪表, 2006, 21(5): 40-43,57 |
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| 作者姓名： | 李绍铭 刘寅虎 |
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| 作者单位： | 安徽工业大学,电气信息学院,马鞍山,243002 |
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| 基金项目： | 安徽省教育厅自然科学基金 |
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| 摘 要： | 针对工业控制领域复杂非线性时变系统．提出了基于改进型RBF神经网络的PID参数在线自整定方法。采用改进型RBF神经网络辨识器在线辨识系统模型，自动调整PID控制器参数，实现系统的智能控制。仿真结果表明，与常规RBF神经网络PID控制方法相比，该方法具有控制精度高、响应速度快的优点，并且具备较强的自适应性和鲁棒性。
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| 关 键 词： | 径向基函数 改进型RBF神经网络 PID控制 最近邻聚类算法 在线自整定 |
| 文章编号： | 1001-9944（2006）05-0040-04 |
| 收稿时间： | 2006-03-28 |
| 修稿时间： | 2006-03-282006-06-26 |
PID Control Based on Ameliorative RBF Neural Network Identification |
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| LI Shao-ming,LIU Yin-hu. PID Control Based on Ameliorative RBF Neural Network Identification[J]. Automation and Instrumentation, 2006, 21(5): 40-43,57 |
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| Authors: | LI Shao-ming LIU Yin-hu |
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| Abstract: | To complex systems which are of characteristics of nonlinearity and time-variation in the industrial control fields,this paper presents a serf-adaptive PID control method based on improved RBF neural network ,which identifies system model online by means of neural network identifier,adjusts automatically parameters of PID controller and achieves intelligence control of system. The simulation result indicates that the system,compared with PID control method based on the conventional RBF neural network, possesses the advantages of high precision, high response speed, great adaptability and robustness. |
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| Keywords: | radial basis function(RBF) ameliorative RBF neural network PID control nearest neighbor-clustering algorithm self-tuning online |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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