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Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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Chengdong Li, Jianqiang Yi, Yisheng Lv and Peiyong Duan, "A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1487-1498, Nov. 2019. doi: 10.1109/JAS.2019.1911543
Citation: Chengdong Li, Jianqiang Yi, Yisheng Lv and Peiyong Duan, "A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1487-1498, Nov. 2019. doi: 10.1109/JAS.2019.1911543

A Hybrid Learning Method for the Data-Driven Design of Linguistic Dynamic Systems

doi: 10.1109/JAS.2019.1911543
Funds:

the National Natural Science Foundation of China 61473176

the National Natural Science Foundation of China 61773246

Natural Science Foundation of Shandong Province for Outstanding Young Talents in Provincial Universities ZR2015JL021

the Taishan Scholar Project of Shandong Province TSQN201812092

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  • In lots of data based prediction or modeling applications, uncertainties and/or noises in the observed data cannot be avoided. In such cases, it is more preferable and reasonable to provide linguistic (fuzzy) predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by numbers. Linguistic dynamic system (LDS) provides a powerful tool for yielding linguistic (fuzzy) results. However, it is still difficult to construct LDS models from observed data. To solve this issue, this paper first presents a simplified LDS whose inputoutput mapping can be determined by closed-form formulas. Then, a hybrid learning method is proposed to construct the data-driven LDS model. The proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method, then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules, and finally adopts multiobjective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy sets. The proposed approach is successfully applied to three real-world prediction applications which are: prediction of energy consumption of a building, forecasting of the traffic flow, and prediction of the wind speed. Simulation results show that the uncertainties in the data can be effectively captured by the linguistic (fuzzy) estimates. It can also be extended to some other prediction or modeling problems, in which observed data have high levels of uncertainties.

     

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  • [1]
    L. A. Zadeh, "Fuzzy logic = computing with words, " IEEE Trans. Fuzzy Systems, vol. 4, no. 2, pp. 103-111, 1996. doi: 10.1109/91.493904
    [2]
    L. A. Zadeh, "From computing with numbers to computing with words –- from manipulation of measurements to manipulation of perceptions, " IEEE Trans. Circuits and Systems–I: Fundamental Theory and Applications, vol. 4, no. 1, pp. 105-119, 1999. http://cn.bing.com/academic/profile?id=97a8d550d75d0622d99d1f4740d53838&encoded=0&v=paper_preview&mkt=zh-cn
    [3]
    J. M. Garibaldi, "The need for fuzzy AI, " IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 610-622, 2019. doi: 10.1109/JAS.2019.1911465
    [4]
    C. Franco, J. T. Rodriguez, and J. Montero, "An ordinal approach to computing with words and the preference-aversion model, " Information Sciences, vol. 258, pp. 239-248, 2014. doi: 10.1016/j.ins.2013.05.021
    [5]
    A. Bilgin, H. Hagras, A. Malibari, M. J. Alhaddad, and D. Alghazzawi, "Towards a linear general type-2 fuzzy logic based approach for computing with words, " Soft Computing, vol. 17, pp. 2203-2222, 2013. doi: 10.1007/s00500-013-1046-2
    [6]
    H. Mo, J. Wang, X. Li, and Z. Wu, "Linguistic dynamic modeling and analysis of psychological health state using interval type-2 fuzzy sets, " IEEE/CAA J. Autom. Sinica, vol. 2, no. 4, pp. 366-373, 2015. doi: 10.1109/JAS.2015.7296531
    [7]
    H. Wang, S. He, C. Li, and X. Pan, "Pythagorean uncertain linguistic variable hamy mean operator and its application to multi-attribute group decision making, " IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 527-539, 2019. doi: 10.1109/JAS.2019.1911408
    [8]
    Y. Wang, "Modeling, analysis and synthesis of linguistic dynamic systems: a computational theory, " in Proc. IEEE Int. Workshop Architecture for Semiotic Modeling and Situation Control in Large Complex System, Monterey, CA, USA: IEEE, pp. 173-178, 1995.
    [9]
    Y. Wang, "Outline of a computing theory for linguistic dynamical systems: towards computing with words, " Int. J. Intelligent Control and Systems, vol. 2, no. 2, pp. 211-224, 1998.
    [10]
    Y. Wang, "Fundamental issues in research of computing with words and linguistic dynamic systems, " Acta Autom. Sinica, vol. 31, no. 6, pp. 844-852, 2005. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdhxb200506005
    [11]
    Y. Wang and Y. Tao, "On linguistic analysis of numerical dynamic systems, " in Proc. IEEE Int. Symposium on Intelligent Control, Vancouver, BC, Canada, pp. 850-855, 2002. http://cn.bing.com/academic/profile?id=aebccbb748e4523ff15b467e1431eba1&encoded=0&v=paper_preview&mkt=zh-cn
    [12]
    Y. Wang, "On the abstraction of conventional dynamic systems: from numerical analysis to linguistic analysis, " Information Science, vol. 171, no. 1-3, pp. 233-259, 2005. doi: 10.1016/j.ins.2004.04.005
    [13]
    H. Mo and F.-Y. Wang, "Linguistic dynamic systems based on computing with words and their stabilities, " Science in China, vol. 52, no.5, pp. 780-796, 2009. http://cn.bing.com/academic/profile?id=943030136f3d2793961d48983bb42e52&encoded=0&v=paper_preview&mkt=zh-cn
    [14]
    H. Mo, "Linguistic dynamic orbits in the time varying universe of discourse, " Acta Autom. Sinica, vol. 38, no. 10, pp. 1585-1594, 2012. doi: 10.3724/SP.J.1004.2012.01585
    [15]
    H. Mo, F.-Y. Wang, and L. Zhao, "On LDS trajectories under one-to-one mappings in interval type-2 fuzzy sets, " Pattern Recognition and Artificial Intelligence, vol. 23, pp. 144-147, 2010. http://cn.bing.com/academic/profile?id=97dfd5073fd7e920f61a8299b7a95a69&encoded=0&v=paper_preview&mkt=zh-cn
    [16]
    H. Mo, F.-Y. Wang, Z. Xiao, and Q. Chen, "Stabilities of linguistic dynamic systems based on interval type-2 fuzzy sets, " Acta Autom. Sinica, vol. 37, no. 8, pp. 1018-1024, 2011. http://cn.bing.com/academic/profile?id=23c1b145755363f5b06363c3893d3927&encoded=0&v=paper_preview&mkt=zh-cn
    [17]
    H. Mo and T. Wang, "Computing with words in generalized interval type-2 fuzzy sets, " Acta Automa. Sinica, vol. 38, no. 5, pp. 707-715, 2012. doi: 10.3724/SP.J.1004.2012.00707
    [18]
    L. Zhao, "Research on the interval type-2 fuzzy method based computing with words and linguistic dynamic systems, " Ph.D. dissertation, Institute of Automation, Chinese Acedemy of Sciences, China, 2009.
    [19]
    L. Zhao, "The class-2 linguistic dynamic trajectories of the interval type-2 fuzzy sets, " in Proc. Int. Conf. Life System Modeling and Intelligent Computing, Wuxi, China, pp. 342-349, 2010. http://cn.bing.com/academic/profile?id=c319730067a66770dd3467ff93483a5e&encoded=0&v=paper_preview&mkt=zh-cn
    [20]
    C. Li, G. Zhang, H. Wang, and W. Ren, "Properties and data-driven design of perceptual reasoning method based linguistic dynamic systems, " Acta Autom. Sinica, vol. 40, no. 10, pp. 2221-232, 2014. doi: 10.1016/S1874-1029(14)60360-8
    [21]
    C. Li, J. Gao, J. Yi, and G. Zhang, "Analysis and design of functionally weighted single-input-rule-modules connected fuzzy inference systems, " IEEE Trans. Fuzzy Systems, vol. 26, no. 1, pp. 56-71, 2018. http://cn.bing.com/academic/profile?id=0bf41b8e3f8d52f64e15e76de3679f73&encoded=0&v=paper_preview&mkt=zh-cn
    [22]
    C. Li, J. Yi, and G. Zhang, "On the monotonicity of interval type-2 fuzzy logic systems, " IEEE Tran. Fuzzy Systems, vol. 22, no.5, pp. 1197 -1212, 2014. http://cn.bing.com/academic/profile?id=966636d5ccd780b9259af041450e4174&encoded=0&v=paper_preview&mkt=zh-cn
    [23]
    C. Li, G. Zhang, J. Yi, F. Shang, and J. Gao, "A fast learning method for data-driven design of interval type-2 fuzzy logic system, " J. Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2705-2715, 2017. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0d9dd01f2093b01230d3511e281410b4
    [24]
    F. Wang, B. Chen, C. Lin, J. Zhang, and X. Meng, "Adaptive neural network finite-time output feedback control of quantized nonlinear systems, " IEEE Trans. Cybernetics, vol. 48, no. 6, pp. 1839-1848, 2017. http://cn.bing.com/academic/profile?id=134027e7d68d8edcd01543a9f98612cd&encoded=0&v=paper_preview&mkt=zh-cn
    [25]
    F. Wang, B. Chen, C. Lin, and X. Li, "Distributed adaptive neural control for stochastic nonlinear multiagent systems, " IEEE Trans. Cybernetics, vol. 47, no. 7, pp. 1795-1803, 2017. doi: 10.1109/TCYB.2016.2623898
    [26]
    X. Wang, A Course in Fuzzy Systems, Prentice-Hall press, USA, 1999.
    [27]
    W. Pedryez and A. V. Vasilakos, "Linguistic models and linguistic modeling, " IEEE Trans. Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 29, no. 6, pp. 745-757, 1999. doi: 10.1109/3477.809029
    [28]
    S. L. Chiu, "Fuzzy model identification based on cluster estimation, " J. Intelligent & Fuzzy Systems, vol. 2, no. 3, pp. 267-278, 1994. http://cn.bing.com/academic/profile?id=0f5876651f9dc8a0c6b4d1f1f6121ad7&encoded=0&v=paper_preview&mkt=zh-cn
    [29]
    R. R. Yager and D. P. Filev, "Generation of fuzzy rules by mountain clustering, " J. Intelligent & Fuzzy Systems, vol. 2, no. 3, pp. 209-219, 1994. http://cn.bing.com/academic/profile?id=30140dde57c05a72a57e837f84bbf700&encoded=0&v=paper_preview&mkt=zh-cn
    [30]
    G. H. Golub and C. F. Van Loan, Matrix Computations (4th Edition), Maryland: The Johns Hopkins University Press, 2013.
    [31]
    K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, "A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-Ⅱ, " in Proc. Int. Conf. Parallel Problem Solving from Nature, pp. 849-858, Berlin Heidelberg, Germany: Springer, 2000. http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_118362906325e77baed31c3f2b8872b6
    [32]
    K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, 2001.
    [33]
    C. Li, Z. Ding, D. Zhao, J. Yi, and G. Zhang, "Building energy consumption prediction: an extreme deep learning approach", vol. 10, no. 10, pp. 1-20, 2017.
    [34]
    C. Li, Z. Ding, J. Yi, Y. Lv, and G. Zhang, "Deep belief network based hybrid model for building energy consumption prediction, " vol. 11, no. 1, pp. 1-26, 2018.
    [35]
    R. K. Jain, K. M. Smith, P. J. Culligan, and J. E. Taylor, "Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy, " Applied Energy, vol. 123, pp. 168-178, 2014. doi: 10.1016/j.apenergy.2014.02.057
    [36]
    S. Naji, S. Shamshirband, H. Basser, A. Keivani, U. J. Alengaram, M. Z. Jumaat, and D. Petkovic, "Application of adaptive neurofuzzy methodology for estimating building energy consumption, " Renewable and Sustainable Energy Reviews, vol. 53, pp. 1520-1528, 2016. doi: 10.1016/j.rser.2015.09.062
    [37]
    Y. Lv, Y. Duan, W. Kang, Z. Li, and F. Y. Wang, "Traffic flow prediction with big data: a deep learning approach, " IEEE Trans. Intelligent Transportation Systems, vol.16, no.2, pp. 865-873, 2015. http://cn.bing.com/academic/profile?id=f1a348782a62f8132071fb2ad1bd56d8&encoded=0&v=paper_preview&mkt=zh-cn
    [38]
    C. Li, Y. Lv, J. Yi, and G. Zhang, "Pruned fast learning fuzzy approach for data-driven traffic flow prediction, " J. Advanced Computational Intelligence and Intelligent Informatics, vol. 20, no. 7, pp. 1181-1191, 2016. doi: 10.20965/jaciii.2016.p1181
    [39]
    L. Dimitriou, T. Tsekeris, and A. Stathopoulos, "Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow, " Transportation Research Part C: Emerging Technologies, vol.16, no.5, pp. 554-573, 2008. doi: 10.1016/j.trc.2007.11.003
    [40]
    C. Li, L. Wang, G. Zhang, H. Wang, and F. Shang, "functional-type single-input-rule-modules connected neural fuzzy system for wind speed prediction, " IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 751-762, 2017. doi: 10.1109/JAS.2017.7510640
    [41]
    I. G. Damousis, M. C. Alexiadis, J. B. Theocharis, and P. S. Dokopoulos, "A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation, " IEEE Trans. Energy Conversation, vol. 19, no. 2, pp. 352-361, 2004. doi: 10.1109/TEC.2003.821865

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    Highlights

    • A simplified linguistic dynamic system (LDS) which has closed-form expression is presented.
    • A hybrid learning method is proposed to construct the data-driven LDS model.
    • The LDS is applied to the linguistic predictions of the energy consumption, traffic flow and wind speed.
    • The proposed approach is easy to implement because it requires minimal human intervention.

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