期刊文献+

基于区间数度量的运动模式建模与控制 被引量:5

Moving pattern measured by interval number for modeling and control
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摘要 针对一类复杂的生产过程,本文作者在前期研究成果中,提出了基于运动模式的建模方法与控制方法的思想.针对该方法中模式类别变量的度量问题,本文提出采用区间数来度量模式类别变量,进而提出了一种基于区间数度量的运动模式建模与控制方法.首先,采用K均值聚类算法对收集的足够长时间内的工况数据进行聚类,得到C个模式类别,进而构成模式刻度"空间".为了描述模式的运动,本文提出了带输入的区间自回归模型(IARX).在此基础上,采用IARX模型建立模式类别变量的控制模型并给出了相应的控制算法.最后,以烧结生产过程为例验证了本文所提建模与控制方法的有效性. On the basis of our previous work on moving pattern-based modeling and control method for a class of complex production processes, we propose using the interval number as the measure of the pattern class variable, and develop a new modeling and control method of the moving pattern measured by the interval number. In this approach, using K-means clustering algorithm, the long-time collection of patterns in operating conditions is clustered into C pattern classes to build a scaled space of patterns. To describe the motion of patterns, we introduce the interval autoregressive model with exogenous input (IARX). With the IARX model, we develop the control model and the control algorithm for the pattern class variable. Practical experimental results are presented for demonstrating the validity and feasibility of the proposed approach of modeling and control.
出处 《控制理论与应用》 EI CAS CSCD 北大核心 2012年第9期1115-1124,共10页 Control Theory & Applications
基金 中央高校基本科研业务费专项资金资助项目(FRF-AS-09-006B) 北京市重点学科发展计划资助项目(XK100080537)
关键词 运动模式 模式运动“空间” 模式类别变量 带输入的区间自回归模型 区间时间序列 模式识别 建模与控制 moving pattern pattern moving 'space' pattern class variable interval ARX model interval time series pattern recognition modeling and control
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参考文献14

  • 1瞿寿德.智能自动化的模式识别方法[J].北京科技大学学报,1998,20(4):385-389. 被引量:6
  • 2瞿寿德,李泽飞,周尚明.模式识别与智能自动化[C].中国智能自动化学术会议论文集.北京:中国自动化学会智能自动化专业委员会,1995:64-66.
  • 3孙一康,瞿寿德.人工智能与过程控制[J].控制理论与应用,1992,9(3):312-313. 被引量:6
  • 4SARIDIS G N, HOFSTADER R F. A pattern recognition approach to the classification on nonlinear systems [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1974, 4(4): 362 - 371.
  • 5CADAPARTHI M, BRAHMANANDAM B, CHATTERJI B N. A pattern recognition approach to model characterization of distributed systems [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1987, 17(3): 488 - 495.
  • 6叶楠,吕勇哉.模式识别在状态估计器中的应用--一类软测量技术[J].仪器仪表学报,1988,9(4):368-374.
  • 7MCBAIN J, T1MUSK M. Feature extraction for novelty detection as applied to fault detection in machinery [J]. Pattern Recognition Let- ter, 2011, 32(7): 1054- 1061.
  • 8SBARBARO D, JOHANSEN T A. Analysis of artificial neural net- works for pattern-based adaptive control [J]. IEEE Transactions on Neural Networks, 2006, 17(5): 1184 - 1193.
  • 9ZHOU X J, CHAI T Y. Pattern-based hybrid intelligent control for rotary kiln process [C]//Proceedings of the 16th IEEE International Conference on Control Applications Part of IEEE Multi-conference on Systems and Control. Piscataway, NJ: IEEE, 2007:30 - 35.
  • 10王聪,陈填锐,刘腾飞.确定学习与基于数据的建模及控制[J].自动化学报,2009,35(6):693-706. 被引量:19

二级参考文献43

  • 1黄琳,秦化淑,郑应平,郑大钟.复杂控制系统理论:构想与前景[J].自动化学报,1993,19(2):129-137. 被引量:24
  • 2Antunes C M, Oliveira A L. Temporal data mining: an overview. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, USA: ACM, 2001. 1-13
  • 3Han J W, Kamber M. Data Mining: Concepts and Techniques (Second Edition). Boston: Morgan Kaufmann, 2006
  • 4Ljung L. System Identification: Theory for the User (Second Edition). New Jersey: Prentice-Hall, 1999
  • 5Gevers M. A personal view of the development of system identification. IEEE Control Systems Magazine, 2006, 26(6): 93-105
  • 6Narendra K S, Parthasavathy K. Identification and control of dynamic systems using neural networks. IEEE Transactions on Neural Networks, 1990, 1(1): 4-27
  • 7Sanner R M, Slotine J J E. Gaussian networks for direct adaptive control. IEEE Transactions on Neural Networks, 1992, 3(6): 837-863
  • 8Sadegh N. A perceptron network for functional identification and control of nonlinear systems. IEEE Transactions on Neural Networks, 1993, 4(6): 982-988
  • 9Kosmatopoulos E B, Polycarpou M M, Christodoulou M A, Ioannou P A. High-order neural network structures for identification of dynamical systems. IEEE Transactions on Neural Networks, 1995, 6(2): 422-431
  • 10Polycarpou M M. Stable adaptive neural control scheme for nonlinear systems. IEEE Transactions on Automatic Control, 1996, 41(3): 447-451

共引文献28

同被引文献45

  • 1修智宏,任光.T-S模糊控制系统的稳定性分析及系统化设计[J].自动化学报,2004,30(5):731-741. 被引量:34
  • 2徐正光.智能自动化的模式识别方法及其工程实现[D].北京:北京科技大学,2001.
  • 3吴晓峰,费敏锐.基于支持向量机预报模型的烧结终点模糊控制[J].浙江大学学报(工学版),2007,41(10):1722-1726. 被引量:6
  • 4Lennart H. Modeling of three-phase dynamic systemsusing complex transfer functions and transfer matrices[J].IEEE Trans on Industrial Electronics, 2007, 54(4): 2239-2248.
  • 5Mohamaed L, Elhassan A. Linearizing and control of athree-phase photovoltaic system with feedback method andintelligent control in state-space[J]. Trans on Electrical andElectronic Materials, 2014, 15(6): 297-304.
  • 6Yu H S, Peng J Z, Tang Y D. Identification ofnonlinear dynamic systems using Hammerstein-type neuralnetwork[J]. Mathematical Problems in Engineering, 2014,10(1): 1-9.
  • 7Cecconello M S, Leite J, Bassanezi R C. Invariant andattractor sets for fuzzy dynamical systems[J]. Fuzzy Setsand Systems, 2015, 265(4): 99-109.
  • 8Zhou Y H. Fuzzy indirect adaptive control using SVM-based multiple models for a class of nonlinear systems[J].Neural Computing and Applications, 2013, 22(3/4): 825-833.
  • 9Liu X F, Lin B. Identification of resonance states ofrotor-bearing system using RQA and optimal binary treeSVM[J]. Neurocomputing, 2015, 152(3): 36-44.
  • 10Xu Z, Liu M, Yang G, et al. Application of interval analysisand evidence theory to fault location[J]. Electric PowerApplications, 2009, 3(1): 77-84.

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