Volume 11 Number 6
December 2014
Article Contents
Majda Ltaief, Anis Messaoud and Ridha Ben Abdennour. Optimal Systematic Determination of Models' Base for Multimodel Representation: Real Time Application. International Journal of Automation and Computing, vol. 11, no. 6, pp. 644-652, 2014. doi: 10.1007/s11633-014-0815-4
Cite as: Majda Ltaief, Anis Messaoud and Ridha Ben Abdennour. Optimal Systematic Determination of Models' Base for Multimodel Representation: Real Time Application. International Journal of Automation and Computing, vol. 11, no. 6, pp. 644-652, 2014. doi: 10.1007/s11633-014-0815-4

Optimal Systematic Determination of Models' Base for Multimodel Representation: Real Time Application

  • Received: 2013-04-04
  • The multimodel approach is a powerful and practical tool to deal with analysis, modeling, observation, emulation and control of complex systems. In the modeling framework, we propose in this paper a new method for optimal systematic determination of models' base for multimodel representation. This method is based on the classification of data set picked out of the considered system. The obtained cluster centers are exploited to provide the weighting functions and to deduce the corresponding dispersions and their models' base. A simulation example and an experimental validation on a semi-batch reactor are presented to evaluate the effectiveness of the proposed method.
  • [1] R. Babuska, H. B. Verbruggen. Fuzzy set methods for local modelling and identification. Multiple Model Approaches to Modelling and Control, R. Marry-Smith, T. A. Johansen, Eds., London: Taylor and Francis, 1997.
    [2] R. Ben Abdennour, P. Borne, M. Ksouri, F. M sahli. Identification et Commande Numérique Des Procédés Industriels, Paris, France: Technip, 2001. (In Franch)
    [3] T. A. Johansen. Operating Regime Based Process Modelling and Identification, Ph.D. dissertation, Norwegian Institute of Technology, Trondheim, Norway, 1994.
    [4] T. A. Johansen, B. A. Foss. Operating regime based process modeling and identification. Computers and Chemical Engineering, vol. 21, no. 2, pp. 159-176, 1997.
    [5] R. Schorten, R. Murry-Smith, R. Bjorgan, H. Gollee. On the interpretation of local models in blended multiple model structures. International Journal of Control, vol. 72, no. 7-8, pp. 620-628, 1999.
    [6] M. Chadli, M. Didier, J. Ragot. Stability analysis and design for continuous-time Takagi-Sugeno control systems. International Journal of Fuzzy Systems, vol. 7, no. 3, pp. 101-109, 2005.
    [7] T. Takagi, M. Sugeno. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, 116-132, 1985.
    [8] F. Delmotte, L. Dubois, P. Borne. A general scheme for multi-model controller using trust. Mathematics and Computers in Simulation, vol. 41, no. 1-2, pp. 173-186, 1996.
    [9] M. Ltaief, K. Abderrahim, R. Ben Abdennour, M. Ksouri. A fuzzy fusion strategy for the multimodel approach application. Wseas Transactions on Circuits and Systems, vol.2, no. 4, pp. 686-691, 2003.
    [10] S. Mezghani, A. Elkamel, P. Borne. Multimodel control of discrete systems with uncertainties. International Journal of Studies in Informatics and Control, vol. 3, no. 2, pp. 7-17, 2001.
    [11] S. Talmoudi, R. Ben Abdennour, K. Abderrahim, M. Ksouri. A New technique of validities' computation for multimodel approach. Wseas Transactions on Circuits and Systems, vol. 2, no. 4, pp. 680-685, 2003.
    [12] S. Talmoudi, K. Abderrahim, R. Ben Abdennour, M. Ksouri. Multimodel approach using neural networks for complex systems modeling and identification. Journal of Nonlinear Dynamics and Systems, vol. 8, no. 3, pp. 299-316, 2008.
    [13] M. Ltaief, K. Abderrahim, R. Ben Abdennour, M. Ksouri. Contributions to the multimodel approach: Systematic determination of a models' base and validities estimation. International Journal of Automation and Systems Engineering, vol. 2, no. 3, 2008.
    [14] D. Filev. Fuzzy modeling of complex systems. International Journal of Approximate Reasoning, vol. 5, no. 3, pp. 281-290, 1991.
    [15] K. Tanaka, M. Sugeno. Stability analysis and design of fuzzy control systems. Fuzzy Sets and Systems, vol. 45, no. 2, pp. 135-156, 1992.
    [16] T. M. Guerra, A. Kruszewski, L. Vermeiren, H. Tirmant. Conditions of output stabilization for nonlinear models in the Takagi-Sugeno's form. Fuzzy Sets and Systems, vol. 157, no. 9, pp. 1248-1259, 2006.
    [17] Ragot J, Marx B, Koenig D. Design of observers for Takagi- Sugeno descriptor systems with unknown inputs and application to fault diagnosis. IET Control Theory and Applications, vol. 1, no. 5, pp. 1487-1495, 2007.
    [18] F. Ahmida, E. H. Tissir. Exponential stability of uncertain T-S fuzzy switched systems with time delay. International Journal of Automation and Computing, vol. 10, no. 1, pp. 32-38, 2013.
    [19] R. Orjuela, D. Maquin, J. Ragot. Nonlinear system identification using uncoupled state multiple-model approach. In Proceedings of Workshop on Advanced Control and Diagnosis, Nancy, France, 2006.
    [20] R. Orjuela, B. Marx, J. Ragot, D. Maquin. State estimation of nonlinear discrete-time systems based on the decoupled multiple model approach. In Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics, Angers, France, 2007.
    [21] R. Orjuela, B. Marx, D. Maquin, J. Ragot. A decoupled multiple model approach for state estimation of nonlinear systems subject to delayed measurements. In Proceedings of the 3rd IFAC Advanced Fuzzy and Neural Network Workshop, IFAC, Valenciennes, France, pp. 29-30, 2008.
    [22] A. Messaoud, M. Ltaief, R. Ben Abdennour. A new contribution of an uncoupled state multimodel predictive control: Experimental validation on a chemical reactor. International Review of Automatic Control, vol. 3, no. 5, pp. 550-559, 2010.
    [23] A. Messaoud, M. Ltaief, R. Ben Abdennour. Partial predictors for the supervision of a multimodel direct generalized predictive control. In Proceedings of the American Control Conference, IEEE, Seattle, USA, pp. 459-464, 2008.
    [24] R.Murray-Smith, T. Johansen. MultipleModel Approaches to Modelling and Control, London: Taylor & Francis, 1997.
    [25] R. Orjuela. Contribution l estimation d état et au diagnostic des systés représentés par des multimodèles, Ph.D. dissertation, Institut National Polytechnique de Lorraine, Nancy, France, 2008. (In Franch)
    [26] S. Bedoui, M. Ltaief, K. Abderrahim. New results on discrete-time delay systems identification. International Journal of Automation and Computing, vol. 9, no. 6, pp. 570-577, 2012.
    [27] S. L. Chiu. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems, vol.2, no. 3, pp. 267-278, 1994.
    [28] S. Abe. Neural Networks and Fuzzy Systems: Theory and Applications, Boston: Kluwer Academic Publishers, 1997.
    [29] K. Narendra, K. Parthasarathy. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 4-27, 1990.
    [30] F. M sahli, C. Fayeche, R. Ben. Abdennour, M. Ksouri. Application of adaptive controllers for the temperature control of a semi-batch reactor. International Journal of Computational Engineering Science, vol. 2, no. 2, pp. 287-307, 2001.
    [31] F. M sahli, R. Ben. Abdennour, M. Ksouri. Experimental nonlinear model based predictive Control For a Class of semi-bath reactor. International Journal of Advanced Manufacturing Technology, vol. 20, no. 6, pp. 459-463, 2002.
    [32] A. Messaoud, M. Ltaief, R. Ben. Abdennour. Supervision based on partial predictors for a multimodel generalized predictive control: Experimental validation on a semi-batch reactor. International Journal of Modelling, Identification and Control, vol. 6, no. 4, pp. 333-340, 2009.
  • 加载中
  • [1] Ziheng Chen, Hongshik Ahn. Item Response Theory Based Ensemble in Machine Learning . International Journal of Automation and Computing, 2020, 17(5): 621-636.  doi: 10.1007/s11633-020-1239-y
    [2] Sajjad Afrakhteh, Mohammad-Reza Mosavi, Mohammad Khishe, Ahmad Ayatollahi. Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Population-physic-based Algorithm . International Journal of Automation and Computing, 2020, 17(1): 108-122.  doi: 10.1007/s11633-018-1158-3
    [3] Negin Alborzi, Fereshteh Poorahangaryan, Homayoun Beheshti. Spectral-spatial Classification of Hyperspectral Images Using Signal Subspace Identification and Edge-preserving Filter . International Journal of Automation and Computing, 2020, 17(2): 222-232.  doi: 10.1007/s11633-019-1188-5
    [4] Anis Khouaja, Hassani Messaoud. Iterative Selection of GOB Poles in the Context of System Modeling . International Journal of Automation and Computing, 2019, 16(1): 102-111.  doi: 10.1007/s11633-016-0984-4
    [5] Ann Smith, Fengshou Gu, Andrew D. Ball. An Approach to Reducing Input Parameter Volume for Fault Classifiers . International Journal of Automation and Computing, 2019, 16(2): 199-212.  doi: 10.1007/s11633-018-1162-7
    [6] Cui-Mei Jiang, Shu-Tang Liu, Fang-Fang Zhang. Complex Modified Projective Synchronization for Fractional-order Chaotic Complex Systems . International Journal of Automation and Computing, 2018, 15(5): 603-615.  doi: 10.1007/s11633-016-0985-3
    [7] Wei-Hua Deng,  Kang Li,  Jing Deng. Identification and Guaranteed Performance Control of Hammerstein Systems over Wireless Channels . International Journal of Automation and Computing, 2016, 13(2): 125-132.  doi: 10.1007/s11633-016-0951-0
    [8] Danasingh Asir Antony Gnana Singh,  Subramanian Appavu Alias Balamurugan,  Epiphany Jebamalar Leavline. An Unsupervised Feature Selection Algorithm with Feature Ranking for Maximizing Performance of the Classifiers . International Journal of Automation and Computing, 2015, 12(5): 511-517.  doi: 10.1007/s11633-014-0859-5
    [9] Min Lin,  Bin Li,  Qiao-Hong Liu. Identification of Eye Movements from Non-frontal Face Images for Eye-controlled Systems . International Journal of Automation and Computing, 2014, 11(5): 543-554.  doi: 10.1007/s11633-014-0827-0
    [10] Nongnuch Poolsawad,  Lisa Moore,  Chandrasekhar Kambhampati. Issues in the Mining of Heart Failure Datasets . International Journal of Automation and Computing, 2014, 11(2): 162-179.  doi: 10.1007/s11633-014-0778-5
    [11] Pavla Bromová,  Petr Škoda,  Jaroslav Vážný. Classification of Spectra of Emission Line Stars Using Machine Learning Techniques . International Journal of Automation and Computing, 2014, 11(3): 265-273.  doi: 10.1007/s11633-014-0789-2
    [12] Zeineb Lassoued,  Kamel Abderrahim. New Results on PWARX Model Identification Based on Clustering Approach . International Journal of Automation and Computing, 2014, 11(2): 180-188.  doi: 10.1007/s11633-014-0779-4
    [13] Junji Satake,  Masaya Chiba,  Jun Miura. Visual Person Identification Using a Distance-dependent Appearance Model for a Person Following Robot . International Journal of Automation and Computing, 2013, 10(5): 438-446.  doi: 10.1007/s11633-013-0740-y
    [14] Saïda Bedoui,  Majda Ltaief,  Kamel Abderrahim. New Results on Discrete-time Delay Systems Identification . International Journal of Automation and Computing, 2012, 9(6): 570-577 .  doi: 10.1007/s11633-012-0681-x
    [15] Appavu Alias Balamurugan Subramanian, S. Pramala, B. Rajalakshmi, Ramasamy Rajaram. Improving Decision Tree Performance by Exception Handling . International Journal of Automation and Computing, 2010, 7(3): 372-380.  doi: 10.1007/s11633-010-0517-5
    [16] Siva S. Sivatha Sindhu, S. Geetha, M. Marikannan, A. Kannan. A Neuro-genetic Based Short-term Forecasting Framework for Network Intrusion Prediction System . International Journal of Automation and Computing, 2009, 6(4): 406-414.  doi: 10.1007/s11633-009-0406-y
    [17] Subramanian Appavu Alias Balamurugan, Ramasamy Rajaram. Effective and Efficient Feature Selection for Large-scale Data Using Bayes’ Theorem . International Journal of Automation and Computing, 2009, 6(1): 62-71.  doi: 10.1007/s11633-009-0062-2
    [18] Alma Lilia Garcia-Almanza,  Edward P. K. Tsang. Evolving Decision Rules to Predict Investment Opportunities . International Journal of Automation and Computing, 2008, 5(1): 22-31.  doi: 10.1007/s11633-008-0022-2
    [19] O. M. Mohamed Vall, R. M'hiri. An Approach to Polynomial NARX/NARMAX Systems Identification in a Closed-loop with Variable Structure Control . International Journal of Automation and Computing, 2008, 5(3): 313-318.  doi: 10.1007/s11633-008-0313-7
    [20] Magda Bogalecka,  Krzysztof Kolowrocki. Probabilistic Approach to Risk Analysis of Chemical Spills at Sea . International Journal of Automation and Computing, 2006, 3(2): 117-124.  doi: 10.1007/s11633-006-0117-6
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Abstract Views (4832) PDF downloads (2055) Citations (0)

Optimal Systematic Determination of Models' Base for Multimodel Representation: Real Time Application

Abstract: The multimodel approach is a powerful and practical tool to deal with analysis, modeling, observation, emulation and control of complex systems. In the modeling framework, we propose in this paper a new method for optimal systematic determination of models' base for multimodel representation. This method is based on the classification of data set picked out of the considered system. The obtained cluster centers are exploited to provide the weighting functions and to deduce the corresponding dispersions and their models' base. A simulation example and an experimental validation on a semi-batch reactor are presented to evaluate the effectiveness of the proposed method.

Majda Ltaief, Anis Messaoud and Ridha Ben Abdennour. Optimal Systematic Determination of Models' Base for Multimodel Representation: Real Time Application. International Journal of Automation and Computing, vol. 11, no. 6, pp. 644-652, 2014. doi: 10.1007/s11633-014-0815-4
Citation: Majda Ltaief, Anis Messaoud and Ridha Ben Abdennour. Optimal Systematic Determination of Models' Base for Multimodel Representation: Real Time Application. International Journal of Automation and Computing, vol. 11, no. 6, pp. 644-652, 2014. doi: 10.1007/s11633-014-0815-4
Reference (32)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return