具有输入故障自适应能力的在线智能建模
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摘要
在工程应用和经济问题分析中,系统辨识建模是应用分析的必不可少的一个步骤。在目前的绝大部分的文献资料中都是考虑系统绝对可靠的情况下就模型的拟合问题进行分析,极少看到相关的文献讨论模型在线运行后因输入变量出现故障后仍设法使模型具备自适应的能力从而使得系统有正确的输出的问题。论文就这个问题进行深入的研究和探讨。
论文分析了飞机油箱剩余油量模型的输入变量之间的多重相关性问题,以离线的方式和多种建模方法建立了系统的测量模型。其中离线方式建立的模型有普通多元最小二乘线性回归模型、向后删除变量筛选变量法建立的多元线性回归模型、偏最小二乘回归分析模型、经多种预处理减少输入变量后建立的自适应神经模糊推理系统模型(ANFIS)。并且从精度和复杂度上对这几种建模方式进行对比分析。
论文重点研究了模型在投入运行后的可靠运行问题。论文提出了几种诊断输入变量是否出现故障和输入变量的故障是否已经消除的算法,并且比较分析了这几种诊断算法的优缺点;论文也给出了几种应对输入变量故障的自适应调整模型系统从而保证系统仍能得到正确的输出的方法。并且把这些保障系统模型可靠运行的算法和方案综合应用于飞机油箱剩余油量的测量系统中。
When it comes to the engineering applications and economy problems analysis, system recognition and modeling is the indispensable premise of the applications and analysis. Most of the documents cast the focus on the aspect of modeling on the real system based on the presupposition of that the system inputs are absolutely reliable. Few documents can be found on the topic of assuring the system in the right track when there is something wrong with the input variables. Such topic has been researched into and discussed in the dissertation.
The problem of multi-co-linearity was analyzed on the process of setting up the model to measure the fuel volume in the airplane tank. Methods of transforming the input variables’domain were applied to solve the multi-co-linearity problem. Backword stepwise regression was applied to the sample data for choosing the input variables. Partial least- squares regression was applied to pick up the principal components. Different ANFIS models based on the dealed data were set up after implementing substractive clustering to obttain the fuzzy rules. The modeling methods proposed above were applied to measure the aircraft fuel volumn during the flight. There was multicollinearity among the sample data of the aircraft fuel volumn. The modeling method which combined the PLS, Substractive Clustering and ANFIS was proposed. The complexity and precision of various types of models including multiple linear regression, backword stepwise regression SCANFIS (Substractive Clustering Adaptive-Network-based Fuzzy Inference Systems) and partial least-squares regression SCANFIS were compared.
The reliability of the model in the operating process was discussed. Several kinds of diagnosis algorithm of judging whether there is any exception with the input variables and whether the exception of the input variables disappear was proposed. Then the respective advantage and disadvantage of such types of algorithm was compared. Various solutions was brought forward to assure the models to attain the right result even if there is something wrong with the input variables. And these solutions were applied to the measuring system for calculating the fuel volume of the airplane.
论文分析了飞机油箱剩余油量模型的输入变量之间的多重相关性问题,以离线的方式和多种建模方法建立了系统的测量模型。其中离线方式建立的模型有普通多元最小二乘线性回归模型、向后删除变量筛选变量法建立的多元线性回归模型、偏最小二乘回归分析模型、经多种预处理减少输入变量后建立的自适应神经模糊推理系统模型(ANFIS)。并且从精度和复杂度上对这几种建模方式进行对比分析。
论文重点研究了模型在投入运行后的可靠运行问题。论文提出了几种诊断输入变量是否出现故障和输入变量的故障是否已经消除的算法,并且比较分析了这几种诊断算法的优缺点;论文也给出了几种应对输入变量故障的自适应调整模型系统从而保证系统仍能得到正确的输出的方法。并且把这些保障系统模型可靠运行的算法和方案综合应用于飞机油箱剩余油量的测量系统中。
When it comes to the engineering applications and economy problems analysis, system recognition and modeling is the indispensable premise of the applications and analysis. Most of the documents cast the focus on the aspect of modeling on the real system based on the presupposition of that the system inputs are absolutely reliable. Few documents can be found on the topic of assuring the system in the right track when there is something wrong with the input variables. Such topic has been researched into and discussed in the dissertation.
The problem of multi-co-linearity was analyzed on the process of setting up the model to measure the fuel volume in the airplane tank. Methods of transforming the input variables’domain were applied to solve the multi-co-linearity problem. Backword stepwise regression was applied to the sample data for choosing the input variables. Partial least- squares regression was applied to pick up the principal components. Different ANFIS models based on the dealed data were set up after implementing substractive clustering to obttain the fuzzy rules. The modeling methods proposed above were applied to measure the aircraft fuel volumn during the flight. There was multicollinearity among the sample data of the aircraft fuel volumn. The modeling method which combined the PLS, Substractive Clustering and ANFIS was proposed. The complexity and precision of various types of models including multiple linear regression, backword stepwise regression SCANFIS (Substractive Clustering Adaptive-Network-based Fuzzy Inference Systems) and partial least-squares regression SCANFIS were compared.
The reliability of the model in the operating process was discussed. Several kinds of diagnosis algorithm of judging whether there is any exception with the input variables and whether the exception of the input variables disappear was proposed. Then the respective advantage and disadvantage of such types of algorithm was compared. Various solutions was brought forward to assure the models to attain the right result even if there is something wrong with the input variables. And these solutions were applied to the measuring system for calculating the fuel volume of the airplane.
引文
[1] 王惠文,偏最小二乘回归方法及其应用[M],国防工业出版社,1999.4.
[2] SOFTIMAGE 编著,宋强,李东帅,孙朵译,建模与变形[M],北京,科学出版社,2004
[3] 邓自立,最优估计理论及其应用:建模、滤波、信息融合估计:modeling, filtering, and information fusion estimation[M],哈尔滨,哈尔滨工业大学出版社,2005
[4] 范剑青,姚琦伟著,陈敏译,非线性时间序列:建模、预报及应用[M],北京,高等教育出版社,2005
[5] M Sugeno, T Yasukawa. A fuzzy-logic-based approach to qualitative modeling, IEEE Trans. on Fuzzy Systems. Vol.1, no.1 pp.7-31.1993
[6] Jyh-Shing, Roger Jang. Input Selection for ANFIS Learning. Proceedings of the IEEE int. Conf. On Fuvy Systems. New Orleans, pp.1493-1499. 1996
[7] S. L Chiu. Selecting input variables for fuzzy models. Journal of Intelligent and fuzzy system. Vo1.4, pp.243-156. 1996
[8] Adam E Gaweda, Jacek M Zurada. Input selection in data-driven fuzzy modeling. IEEE Int. fuzzy systems conf. pp.1251-1254.2001
[9]A. E. Gaweda, J. M. Zurada and R. Setiono, “Input Selection in Data-driven Fuzzy Modeling,” IEEE International Fuzzy Conference, 2001, pp.1251-1254
[10]M.Sugeno and T.Yasukawa, “A Fuzzy Logic-based Approach to Qualitative Modeling,” IEEE Transaction on Fuzzy Systems, Vol.1, No.1, 1993, pp.7-31
[11]Y. Lin, G.Cunningham and S.V.Coggeshall, “Input Variable Identification-Fuzzy Curves and Fuzzy Surfaces,” Fuzzy Sets and Systems, 82,1996, pp.65-71
[12]A.H. Sung, J.Lin, “Performance Comparison of Neural Network Models for Engineering Problems.” IEEE International Conference on Systems, Man and Cybernetics, 4, 1997, pp.12-15
[13]尹朝庆,尹皓编著,《人工智能与专家系统》,中国水利水电出版社,2002.01
[14]J.S.R. Jang, C.T.Sun, E.Mizutani. “Neuro-Fuzzy and Soft Computing A Computional Approach to Learning and Machine Intelligence”. Publish by Prentice Hall. 1997
[15] Zadeh, Lofti A. "Fuzzy sets." Information and Control (1965): 338-353.
[16]R. Jang. “ANFIS: Adaptive-Network-based Fuzzy Inference System.” IEEE Trans. On Systems, Man& Cybernetics, may 1993.
[17]R. R. Yager, D. P. Filev. “ Approximate clustering via the mountain method.” IEEE Trans. On systems, man & Cybernetics, 24 :1279-1284, 1992.
[18] J.-S. Roger Jang. “Structure determination in fuzzy modeling: a fuzzy CART approach”, In proceedings of IEEE International conference on Fuzzy Systems, Orlando,Florida, June 1994.
[19]R. R. Yager, D. P. Filev. “ Generation of fuzzy rules by mountain clustering.” Journal of Intelligent and Fuzzy Systems, 2:209-219, 1994.
[20]R. R. Yager, D. P. Filev. “ Essentials of fuzzy modeling and control.” John Wilwy &Sons, Inc, 1994.
[21] Stephen L. Chiu, “A Cluster Estimation Method with Extension to Fuzzy Model Identification” IEEE Trans .1994.
[22]A.K.Jain, M.N.Murty, P.J.Flynn. Data Clustering: A Survey. ACM Comput. Surv 31:264-323,1999
[23]王实,高文. 数据挖掘中的聚类方法,计算机科学,2000(4),42-45
[24]J. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981
[25]Agrawal R, Imielinske T, Swami A. Mining association rules between sets of items in large databases [A].Proceedings of the ACM SIGMOD International Conference on the Management of Data[C],1993:207-216.
[26]JiaWei Han, Micheline Kamber 著,范明、孟小峰等译,《数据挖掘:概念与技术》,机械工业出版社,2001
[27]Srikant R, Agrawal R. Mining quantitative association rules in large relational tables. ACM SIGMOD Issues, 1996,25(2):1-12.
[28]U. Fayyad et al. (eds.) Advances in Dnowledge Discovery and Data Mining, AAAI/MIT Press,Boston,MA,1995
[29]R.R. Yager, “Fuzzy Summaries in Database Mining,” in Proc. of the 11th Conf. On Artificial Intelligence for Application, Los Angeles, California,Feb.1995,pp.265-269.
[30]W. Pedrycz, “Data Mining and Fuzzy Modeling,” in Proc. of 1996 Biennial Conf. of the North American Fuzzy Information Processing Society (NAFIPS), Berkeley, California, June 1996,pp.263-267.
[31]何新贵,数据采掘中的模糊技术,计算机科学,1998 年,Vol 25, P129-131
[32]Chen Ning, Chen An, Zhou Long-xiang, Efficient Algorithm for Mining Fuzzy Rules in LargeRelational Databases. Journal of Software Vol 12, No7. P949-959
[33]杨和雄,李崇文编著,模糊数学和它的应用,天津科学技术出版社,1993.10,天津
[34]K.C.C. Chan, and W.H.Au, “Mining Fuzzy Association Rules,” to appear in Proc. of the 6th ACM Int’l Conf. on Information and Knowledge Management (CIKM’97), Las Vegas, Nevada, Nov.1997.
[35]孙增圻 等编著,智能控制理论与技术,清华大学出版社、广西科学技术出版社,1997
[2] SOFTIMAGE 编著,宋强,李东帅,孙朵译,建模与变形[M],北京,科学出版社,2004
[3] 邓自立,最优估计理论及其应用:建模、滤波、信息融合估计:modeling, filtering, and information fusion estimation[M],哈尔滨,哈尔滨工业大学出版社,2005
[4] 范剑青,姚琦伟著,陈敏译,非线性时间序列:建模、预报及应用[M],北京,高等教育出版社,2005
[5] M Sugeno, T Yasukawa. A fuzzy-logic-based approach to qualitative modeling, IEEE Trans. on Fuzzy Systems. Vol.1, no.1 pp.7-31.1993
[6] Jyh-Shing, Roger Jang. Input Selection for ANFIS Learning. Proceedings of the IEEE int. Conf. On Fuvy Systems. New Orleans, pp.1493-1499. 1996
[7] S. L Chiu. Selecting input variables for fuzzy models. Journal of Intelligent and fuzzy system. Vo1.4, pp.243-156. 1996
[8] Adam E Gaweda, Jacek M Zurada. Input selection in data-driven fuzzy modeling. IEEE Int. fuzzy systems conf. pp.1251-1254.2001
[9]A. E. Gaweda, J. M. Zurada and R. Setiono, “Input Selection in Data-driven Fuzzy Modeling,” IEEE International Fuzzy Conference, 2001, pp.1251-1254
[10]M.Sugeno and T.Yasukawa, “A Fuzzy Logic-based Approach to Qualitative Modeling,” IEEE Transaction on Fuzzy Systems, Vol.1, No.1, 1993, pp.7-31
[11]Y. Lin, G.Cunningham and S.V.Coggeshall, “Input Variable Identification-Fuzzy Curves and Fuzzy Surfaces,” Fuzzy Sets and Systems, 82,1996, pp.65-71
[12]A.H. Sung, J.Lin, “Performance Comparison of Neural Network Models for Engineering Problems.” IEEE International Conference on Systems, Man and Cybernetics, 4, 1997, pp.12-15
[13]尹朝庆,尹皓编著,《人工智能与专家系统》,中国水利水电出版社,2002.01
[14]J.S.R. Jang, C.T.Sun, E.Mizutani. “Neuro-Fuzzy and Soft Computing A Computional Approach to Learning and Machine Intelligence”. Publish by Prentice Hall. 1997
[15] Zadeh, Lofti A. "Fuzzy sets." Information and Control (1965): 338-353.
[16]R. Jang. “ANFIS: Adaptive-Network-based Fuzzy Inference System.” IEEE Trans. On Systems, Man& Cybernetics, may 1993.
[17]R. R. Yager, D. P. Filev. “ Approximate clustering via the mountain method.” IEEE Trans. On systems, man & Cybernetics, 24 :1279-1284, 1992.
[18] J.-S. Roger Jang. “Structure determination in fuzzy modeling: a fuzzy CART approach”, In proceedings of IEEE International conference on Fuzzy Systems, Orlando,Florida, June 1994.
[19]R. R. Yager, D. P. Filev. “ Generation of fuzzy rules by mountain clustering.” Journal of Intelligent and Fuzzy Systems, 2:209-219, 1994.
[20]R. R. Yager, D. P. Filev. “ Essentials of fuzzy modeling and control.” John Wilwy &Sons, Inc, 1994.
[21] Stephen L. Chiu, “A Cluster Estimation Method with Extension to Fuzzy Model Identification” IEEE Trans .1994.
[22]A.K.Jain, M.N.Murty, P.J.Flynn. Data Clustering: A Survey. ACM Comput. Surv 31:264-323,1999
[23]王实,高文. 数据挖掘中的聚类方法,计算机科学,2000(4),42-45
[24]J. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981
[25]Agrawal R, Imielinske T, Swami A. Mining association rules between sets of items in large databases [A].Proceedings of the ACM SIGMOD International Conference on the Management of Data[C],1993:207-216.
[26]JiaWei Han, Micheline Kamber 著,范明、孟小峰等译,《数据挖掘:概念与技术》,机械工业出版社,2001
[27]Srikant R, Agrawal R. Mining quantitative association rules in large relational tables. ACM SIGMOD Issues, 1996,25(2):1-12.
[28]U. Fayyad et al. (eds.) Advances in Dnowledge Discovery and Data Mining, AAAI/MIT Press,Boston,MA,1995
[29]R.R. Yager, “Fuzzy Summaries in Database Mining,” in Proc. of the 11th Conf. On Artificial Intelligence for Application, Los Angeles, California,Feb.1995,pp.265-269.
[30]W. Pedrycz, “Data Mining and Fuzzy Modeling,” in Proc. of 1996 Biennial Conf. of the North American Fuzzy Information Processing Society (NAFIPS), Berkeley, California, June 1996,pp.263-267.
[31]何新贵,数据采掘中的模糊技术,计算机科学,1998 年,Vol 25, P129-131
[32]Chen Ning, Chen An, Zhou Long-xiang, Efficient Algorithm for Mining Fuzzy Rules in LargeRelational Databases. Journal of Software Vol 12, No7. P949-959
[33]杨和雄,李崇文编著,模糊数学和它的应用,天津科学技术出版社,1993.10,天津
[34]K.C.C. Chan, and W.H.Au, “Mining Fuzzy Association Rules,” to appear in Proc. of the 6th ACM Int’l Conf. on Information and Knowledge Management (CIKM’97), Las Vegas, Nevada, Nov.1997.
[35]孙增圻 等编著,智能控制理论与技术,清华大学出版社、广西科学技术出版社,1997