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基于BP神经网络预测的自相关过程MMSE控制

MMSE Control of Autocorrelation Process Based on BP Neural Network Prediction
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摘要 自相关过程的质量控制,常采用基于MMSE控制器的SPC/APC整合方法进行监测.而MMSE的设计,依赖于自相关过程预测的精度.对自相关过程采用BP神经网络进行预测,APC过程采用MMSE控制器进行过程调整,最后采用SPC进行过程监测.仿真实验表明:该方法能够有效去除自相关的影响,是一种有效的自相关过程监测方法,有实际应用价值. Autocorrelation process quality control often uses SPC/APC integrated method based on MMSE controller to monitor. The design of the MMSE controller depends on the prediction accuracy of autocorrelation process. Autocorrelation process is predicted by BP neural network in this paper, MMSE controller is utilized for adjusting process in APC process, and SPC is utilized for monitoring process. Simulation results show that this method can effectively remove the impact of autocorrelation. It is an effective process monitoring method of autocorrelation process, and has practical value.
出处 《河南科学》 2013年第10期1647-1651,共5页 Henan Science
基金 河南省科技攻关计划项目(09210221014) 河南省基础与前沿技术计划项目(112300410048 122300410083)
关键词 自相关过程 SPC APC整合 MMSE控制器 BP神经网络 autocorrelation process SPC/APC integration MMSE controller BP neural network
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