[1]张秀玲,逄宗鹏,李少清,等.ANFIS的板形控制动态影响矩阵方法[J].智能系统学报,2010,5(4):360-365.
ZHANG Xiu-ling,PANG Zong-peng,LI Shao-qing,et al.A dynamic influence matrix method for flatness control based on adaptivenetworkbased fuzzy inference systems[J].CAAI Transactions on Intelligent Systems,2010,5(4):360-365.
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ZHANG Xiu-ling,PANG Zong-peng,LI Shao-qing,et al.A dynamic influence matrix method for flatness control based on adaptivenetworkbased fuzzy inference systems[J].CAAI Transactions on Intelligent Systems,2010,5(4):360-365.
ANFIS的板形控制动态影响矩阵方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
5
期数:
2010年第4期
页码:
360-365
栏目:
学术论文—人工智能基础
出版日期:
2010-08-25
- Title:
- A dynamic influence matrix method for flatness control based on adaptivenetworkbased fuzzy inference systems
- 文章编号:
- 1673-4785(2010)04-0360-06
- 关键词:
- 板形控制; 自适应神经模糊推理系统; 影响矩阵; 聚类; 模糊
- Keywords:
- flatness control; adaptive neurofuzzy inference system; influence matrix; clustering; fuzzy
- 分类号:
- TP18
- 文献标志码:
- A
- 摘要:
- 针对板形控制系统的非线性和强耦合性，以及传统效应函数法和板形静态影响矩阵法的不足，通过对大量生产实测数据的计算和分析，提出了板形控制的动态影响矩阵法.通过基于减法聚类的ANFIS（自适应神经模糊推理系统）的板形动态矩阵预测模型，在线求得不断变化的影响矩阵，兼顾了板带生产的实时性与复杂性，仿真实验验证了其有效性.
- Abstract:
- Flatness control systems have both strong nonlinearity and coupling. Unfortunately traditional effective function methods and the static influence matrix of flatness can not effectively solve such problems. After analysis of a large volume of production data a new method was proposed, a dynamical influence matrix method for the flatness controller. Using the predictive model of the dynamic flatness matrix, and incorporating the subtractive clustering of an adaptive neurofuzzy inference system (ANFIS), the influence matrix was calculated in real time. Both the need for realtime results and the complexities of strip steel production were accommodated. Simulations confirmed the validity of the proposed method.
- 参考文献/References:
-
［1］ 张秀玲.冷带轧机板形智能识别与智能控制研究［D］. 秦皇岛:燕山大学，2002.
?ZHANG Xiuling. Research on intelligent control and recognition of flatness for cold strip mill［D］.Qinhuangdao: Yanshan University， 2002.
［2］何海涛.宽带钢冷轧机板形在线控制智能模型的研究与应用［D］. 秦皇岛:燕山大学，2008.
HE Haitao. Research on flatness online intelligent control for the wide strip steel cold mill［D］. Qinhuangdao: Yanshan University , 2008.
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LIU Jianchang, CHEN Yingying, ZHANG Ruiyou. Intelligent flatnesscontroller based on PSOBP network［J］. Journal of Control Theory and Application, 2007, 24(4): 674-678.
?［4］RINGWOOD J V. Shape control systems for Sendzimir steel mills［J］.IEEE Transactions on Control Systems Technology, 2000, 8(1): 70-86.
?［5］ZARATE L E, BITTENCOUT F R. Neural networks and fuzzy rules based control for cold rolling process via sensitivity factors［C］//27th Annual Conference of the IEEE Indus trial Electronics Society. Piscataway, USA, 2001: 64-69.
［6］LIU Hongmin, ZHANG Xiuling, WANG Yingrui. Transfer matrix method of flatness control for strip mills［J］. Journal of Materials Processing Technology, 2005, 166(2): 237-242.
［7］贾春玉,单修迎,牛召平.自调整动态神经网络模型及其在带材板形预测中的应用［J］. 钢铁研究学报，2006, 18(12): 50-53.
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JIA Chunyu, SHAN Xiuying, LIU Hongmin, QIU Gejun. Application of fuzzy nerve flatness control model in cold rolling［J］. Metallurgical Equipment, 2008, 1: 1-5.
［9］吴晓莉,林哲辉. MATLAB辅助模糊系统设计［M］. 西安:西安电子科技大学出版社，2002：62-93.
［10］YING L C, PAN M C. Using adaptive network based fuzzy inference system to forecast regional electricity loads［J］. Energy Conversion and Management, 2008, 49: 205-211
［11］张阿卜.基于减法聚类和自适应神经模糊推理系统的递阶模糊系统的设计［J］. 控制理论与应用，2004, 21(3): 415-418.
ZHANG Abu. Design of hierarchical fuzzy system via subtractive clustering and ANFIS［J］.Journal of Control Theory and Application, 2004, 21(3): 415-418.
［12］杨新,张陶红,余刚,柴天佑.基于ANFIS的选矿产品成本预测模型［J］. 系统仿真学报，2007, 19(24): 5688-5691.
YANG Xin, ZHANG Taohong, YU Gang, CHAI Tianyou.Mineral processing product cost forecasting model based on ANFIS［J］. Journal of System Simulation, 2007, 19(24): 5688-5691.
- 相似文献/References:
-
[1]张秀玲,陈丽杰,逄宗朋,等.RBF神经网络的板形预测控制[J].智能系统学报,2010,5(1):70.
ZHANG Xiu-ling,CHEN Li-jie,PANG Zong-peng,et al.A predictive system for process control of flatness in rolling mills using a radial basis function network[J].CAAI Transactions on Intelligent Systems,2010,5():70.
备注/Memo
收稿日期：2009-06-13.
基金项目：国家自然科学基金资助项目（50675186）.
通信作者：张秀玲.E-mail: zxlysu@yahoo.com.cn.
作者简介：
?张秀玲，女，1968年生，教授，博士，主要研究方向为神经网络智能控制研究，获国家科技进步二等奖1项，省部级科技进步一等奖、二等奖各1项，发表学术论文60余篇.
逄宗朋，男，1983年生,硕士研究生，主要研究方向为模糊神经网络优化板形设计.
更新日期/Last Update:
2010-09-20