| 基于RBF神经网络的PID整定 |
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| 引用本文： | 李广军,张翠芳,申元霞.基于RBF神经网络的PID整定[J].佳木斯大学学报,2005,23(3):354-357. |
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| 作者姓名： | 李广军 张翠芳 申元霞 |
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| 作者单位： | 西南交通大学计算机与通信工程学院,四川成都610031 |
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| 摘 要： | 针对非线性系统，采用了基于径向基函数（RBF）神经网络的PID整定，用遗传算法优化RBF神经网络，仿真结果表明，基于遗传算法优化的RBF神经网络PID整定收敛速度快，整定效果优于基于梯度下降法优化的RBF神经网络PID整定。
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| 关 键 词： | 径向基函数神经网络（RBFNN） PID整定 梯度下降法 遗传算法 |
| 文章编号： | 1008-1402（2005）03-0354-04 |
| 收稿时间： | 2005-03-25 |
| 修稿时间： | 2005年3月25日 |
Adaptive PID Control Based on RBF Identification |
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| LI Guang-jun,ZHANG Cui-fang,SHEN Yuan-xia.Adaptive PID Control Based on RBF Identification[J].Journal of Jiamusi University(Natural Science Edition),2005,23(3):354-357. |
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| Authors: | LI Guang-jun ZHANG Cui-fang SHEN Yuan-xia |
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| Institution: | School of Computer and Comm.Eng.,Southewest Jiaotong University,Chengdu 610031,China |
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| Abstract: | To nonlinear system, a PID control system based on RBF Identification is used, and genetic algorithms (GA) are used to optimize RBF neural networks. The simulation results show that GA converge quite quickly, the controller effects of PID control which is based on RBF identification optimized by GA are better than those of PID control which is based on RBF identification optimized by the adaptive grads-dropping algorithms. |
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