Sinter Product Rate Forecasting Based on T-S Fuzzy Model

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• 英文论文题名：Sinter Product Rate Forecasting Based on T-S Fuzzy Model
• 论文作者：Qi Lei ; GaiWen Zhao ; Xin Chen ; Min Wu
• 英文论文作者：Qi Lei ; GaiWen Zhao ; Xin Chen ; Min Wu ; School of Information Science and Engineering ; Central South University ; School of Automation ; China University of Geosciences ; Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
• 年：2017
• 作者机构：School of Information Science and Engineering,Central South University;School of Automation,China University of Geosciences;Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems;
• 英文论文关键词：Sinter Product Rate Forecasting ; T-S Fuzzy Model ; Fuzzy c-means ; Recursive Least Squares(RLS)
• 会议召开时间：2017-07-26
• 会议录名称：第36届中国控制会议论文集（B）
• 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
• 语种：英文
• 分类号：TF532;TP18
• 学会代码：KZLL
• 会议名称：第36届中国控制会议
• 会议地点：中国辽宁大连
• 主办单位：中国自动化学会控制理论专业委员会
• 学会名称：中国自动化学会控制理论专业委员会
• 页数：5
• 文件大小：295k
• 原文格式：D
• 会议级别：全国

摘要

Through the analysis of sintering process and the relationship between sintering thermal state and sinter product rate, this paper determines the key factors influencing the sinter product rate, and proposes a method of sinter product rate forecasting based on T-S fuzzy model. Based on the extraction of the characteristic parameters of the thermal state of sintering process, the input of the model is determined. Then, the antecedent and the consequent parameters of the forecasting model are identified by the fuzzy c-means(FCM) clustering algorithm and the recursive least squares method(RLS). Finally, the T-S fuzzy model for sinter product rate forecasting is obtained. The proposed forecasting method is of value through the analysis of the forecast ing results. The forecasting results are also compared with the support vector machine(SVM) and the back propagation neural network(BPNN) methods. The results show that the proposed T-S fuzzy model can effectively improve the precision of the sinter product rate forecasting.
Through the analysis of sintering process and the relationship between sintering thermal state and sinter product rate, this paper determines the key factors influencing the sinter product rate, and proposes a method of sinter product rate forecasting based on T-S fuzzy model. Based on the extraction of the characteristic parameters of the thermal state of sintering process, the input of the model is determined. Then, the antecedent and the consequent parameters of the forecasting model are identified by the fuzzy c-means(FCM) clustering algorithm and the recursive least squares method(RLS). Finally, the T-S fuzzy model for sinter product rate forecasting is obtained. The proposed forecasting method is of value through the analysis of the forecast ing results. The forecasting results are also compared with the support vector machine(SVM) and the back propagation neural network(BPNN) methods. The results show that the proposed T-S fuzzy model can effectively improve the precision of the sinter product rate forecasting.

引文

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