| 基于RBF神经网络预测的MMSE控制 |
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| 引用本文： | 禹建丽,张宗伟. 基于RBF神经网络预测的MMSE控制[J]. 东南大学学报(自然科学版), 2009, 0(Z1) |
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| 作者姓名： | 禹建丽 张宗伟 |
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| 作者单位： | 中原工学院电子信息学院; |
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| 基金项目： | 河南省科技攻关资助项目(072102260020) |
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| 摘 要： | 为了降低过程干扰造成的生产过程波动,研究了一种基于径向基(RBF)神经网络预测的MMSE控制器.首先,采用基于k-聚类学习算法的3层优化径向基网络结构,预测过程干扰时间序列,在此基础上设计MMSE控制器,将其作为EPC过程调整策略,应用于一个化工生产过程的SPC与EPC集成控制系统.然后,采用SPC控制图监测经上述MMSE控制器调整后过程输出并与传统MMSE控制器调整后的过程输出作比较.结果表明,径向基(RBF)神经网络可提高过程干扰预测精度,改进MMSE控制器的控制性能,减小过程波动,提升SPC与EPC集成控制的能力.
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| 关 键 词： | RBF神经网络 预测 MMSE控制 SPC/EPC |
MMSE control based on RBF neural network prediction |
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| Yu Jianli Zhang Zongwei. MMSE control based on RBF neural network prediction[J]. Journal of Southeast University(Natural Science Edition), 2009, 0(Z1) |
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| Authors: | Yu Jianli Zhang Zongwei |
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| Affiliation: | Yu Jianli Zhang Zongwei(School of Electronic Information,Zhongyuan University of Technology,Zhengzhou 450007,China) |
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| Abstract: | To reduce the variability in manufacturing process caused by process disturbance,a design method for the minimum mean square error(MMSE) controller based on radial basis function(RBF) neural network prediction is presented.First,an optimal three-layer RBF neural network based on the k-cluster learning algorithm is constructed to predict the process disturbance.A MMSE controller is designed as one part of the process adjusting strategy for engineering process control(EPC).Then,statistical process control(SPC... |
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| Keywords: | radial basis function neural network prediction minimum mean square error control statistical process control/engineering process control |
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