| 基于蚁群算法优化RBF神经网络的汽温系统控制 |
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| 引用本文： | 郎宪明,屈宝存,吴传国,张勇强.基于蚁群算法优化RBF神经网络的汽温系统控制[J].哈尔滨理工大学学报,2010,15(6):47-50. |
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| 作者姓名： | 郎宪明 屈宝存 吴传国 张勇强 |
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| 作者单位： | 辽宁石油化工大学信息与控制工程学院,辽宁抚顺113001 |
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| 摘 要： | 针对火电厂主蒸汽温度模型不确定性的问题,提出一种智能复合控制方法.采用粒子群算法优化蚁群算法参数,将改进蚁群算法对径向基神经网络PID的权值进行优化,从而实现了对主蒸汽温度的动态控制.仿真结果表明,基于改进蚁群算法优化的径向基神经网络PID控制器使被控系统具有快速响应速度和很好的抗干扰性能,证明了该方法的有效性.
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| 关 键 词： | 主蒸汽温度 蚁群算法 粒子群算法 径向基神经网络 |
Main Steam Temperature Control Using RBF Neural Network Based on Ant Colony Optimization Algorithm |
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| LANG Xian-ming,QU Bao-cun,WU Chuan-guo,ZHANG Yong-qiang.Main Steam Temperature Control Using RBF Neural Network Based on Ant Colony Optimization Algorithm[J].Journal of Harbin University of Science and Technology,2010,15(6):47-50. |
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| Authors: | LANG Xian-ming QU Bao-cun WU Chuan-guo ZHANG Yong-qiang |
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| Affiliation: | (School of Information and Control Engineering,Liaoning Shihua University,Fushun 113001,China) |
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| Abstract: | Directing against the features of uncertainty in the steam temperature control of thermal power plants,an intelligent control method has been put forward.The improved ant colony optimization algorithm with PSO algorithm is put forward to optimize the weights of network PID controller,thus realizing the main steam temperature dynamic control.The simulation results show that the PID controller using RBF neural network based on ant colony algorithm with PSO algorithm has the advantages of high rapid response,and stronger anti-disturbance performance,proving the effectiveness of this method. |
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| Keywords: | main-steam temperature ant colony algorithm particle swarm optimization algorithm radial basis function neural networks |
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