Data-Driven Optimization of SIRMs Connected Neural-Fuzzy System with Application to Cooling and Heating Loads Prediction

  • Chengdong LiEmail author
  • Weina Ren
  • Jianqiang Yi
  • Guiqing Zhang
  • Fang Shang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)


In modeling, prediction and control applications, the single-input-rule-modules (SIRMs) connected fuzzy inference method can efficiently tackle the rule explosion problem that conventional fuzzy systems always face. In this paper, to improve the learning performance of the SIRMs method, a neural structure is presented. Then, based on the least square method, a novel parameter learning algorithm is proposed for the optimization of the SIRMs connected neural-fuzzy system. Further, the proposed neural-fuzzy system is applied to the cooling and heating loads prediction which is a popular multi-variable problem in the research domain of intelligent buildings. Simulation and comparison results are also given to demonstrate the effectiveness and superiority of the proposed method.


data-driven optimization single input rule module least square method fuzzy system cooling and heating loads 


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  1. 1.
    Wang, D.: Robust data-driven modeling approach for real-time final product quality prediction in batch process operation. IEEE Trans. on Industrial Informatics 7(2), 371–377 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Wang, Z., Liu, D.: A data-based state feedback control method for a class of nonlinear systems. IEEE Trans. on Industrial Informatics 9(4), 2284–2292 (2013)CrossRefGoogle Scholar
  3. 3.
    Hou, Z.S., Wang, Z.: From model-based control to data-driven control: survey, classification and perspective. Information Sciences 235, 3–35 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Song, R., Xiao, W., Zhang, H.: Multi-objective optimal control for a class of unknown nonlinear systems based on finite-approximation-error ADP algorithm. Neurocomputing 119, 212–221 (2013)CrossRefGoogle Scholar
  5. 5.
    Mendel, J.M.: Uncertain rule-based fuzzy logic systems: introduction and new directions. Prentice-Hall, Upper Saddle River (2001)zbMATHGoogle Scholar
  6. 6.
    Wang, L.-X.: Analysis and design of hierarchical fuzzy systems. IEEE Trans. on Fuzzy Systems 7(9), 617–624 (1999)CrossRefGoogle Scholar
  7. 7.
    Joo, M.G., Lee, J.S.: A class of hierarchical fuzzy systems with constraints on the fuzzy rules. IEEE Trans. on Fuzzy Systems 13(2), 194–203 (2005)CrossRefGoogle Scholar
  8. 8.
    Hagras, H.: A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans. on Fuzzy Systems 12(4), 524–539 (2004)CrossRefGoogle Scholar
  9. 9.
    Yi, J., Yubazaki, N.: Stabilization fuzzy control of inverted pendulum systems. Artificial Intelligence in Engineering 14, 153–163 (2000)CrossRefGoogle Scholar
  10. 10.
    Yi, J., Yubazaki, N., Hirota, K.: Upswing and stabilization control of inverted pendulum system based on the sirms dynamically connected fuzzy inference model. Fuzzy Sets and Systems 122, 139–152 (2001)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Yi, J., Yubazaki, N., Hirota, K.: Stabilization control of series-type double inverted pendulum systems using the sirms dynamically connected fuzzy inference model. Artificial Intelligence in Engineering 15, 297–308 (2001)CrossRefGoogle Scholar
  12. 12.
    Yi, J., Yubazaki, N., Hirota, K.: A proposal of sirms dynamically connected fuzzy inference model for plural input fuzzy control. Fuzzy Sets and Systems 125, 79–92 (2002)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Yi, J., Yubazaki, N., Hirota, K.: A new fuzzy controller for stabilization of parallel-type double inverted pendulum system. Fuzzy Sets and Systems 126, 105–119 (2002)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Yi, J., Yubazaki, N., Hirota, K.: Anti-swing and positioning control of overhead traveling crane. Information Sciences 155, 19–42 (2003)CrossRefGoogle Scholar
  15. 15.
    Seki, H., Ishii, H., Mizumoto, M.: On the generalization of single input rule modules connected type fuzzy reasoning method. IEEE Trans. on Fuzzy Systems 16(5), 1180–1187 (2008)CrossRefGoogle Scholar
  16. 16.
    Li, C., Yi, J.: SIRMs based interval type-2 fuzzy inference systems: properties and application. International Journal of Innovative Computing, Information and Control 6(9), 4019–4028 (2010)Google Scholar
  17. 17.
    Seki, H., Mizumoto, M., Yubazaki, N.: On the property of single input rule modules connected type fuzzy reasoning method. IEICE Trans. on Fundamentals J89-A, 557–565 (2006)Google Scholar
  18. 18.
    Seki, H., Mizumoto, M.: On the equivalence conditions of fuzzy inference methods—Part 1: basic concept and definition. IEEE Trans. on Fuzzy Systems 19(6), 1097–1106 (2011)CrossRefGoogle Scholar
  19. 19.
    Li, C., Zhang, G., Yi, J., Wang, T.: On the properties of SIRMs connected type-1 and type-2 fuzzy inference systems. In: 2011 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2011, pp. 1982–1988 (2011)Google Scholar
  20. 20.
    Seki, H., Ishii, H., Mizumoto, M.: On the monotonicity of single input type fuzzy reasoning methods. IEICE Trans. on Fundamentals E90-A, 1462–1468 (2007)CrossRefGoogle Scholar
  21. 21.
    Seki, H., Ishii, H., Mizumoto, M.: On the monotonicity of fuzzy-inference methods related to T–S inference method. IEEE Trans. on Fuzzy Systems 18(3), 629–634 (2010)CrossRefGoogle Scholar
  22. 22.
    Li, C., Yi, J., Wang, T.: Stability analysis of SIRMs based type-2 fuzzy logic control systems. In: The 2010 IEEE World Congress on Computational Intelligence (WCCI 2010), pp. 2913–2918 (2010)Google Scholar
  23. 23.
    Seki, H.: Nonlinear identification using single input connected fuzzy inference model. Procedia Computer Science 22, 1121–1125 (2013)CrossRefGoogle Scholar
  24. 24.
    Seki, H., Mizumoto, M.: SIRMs connected fuzzy inference method adopting emphasis and suppression. Fuzzy Sets and Systems 215, 112–126 (2013)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Li, C., Wang, M., Zhang, G.: Prediction of thermal comfort using SIRMs connected type-2 fuzzy reasoning method. ICIC Express Letters 7(4), 1401–1406 (2013)Google Scholar
  26. 26.
    Tsanas, A., Xifara, A.: Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings 49, 560–567 (2012)CrossRefGoogle Scholar
  27. 27.
    Nelles, O.: Nonlinear system identification, pp. 35–67. Springer, Berlin (2001)Google Scholar

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Authors and Affiliations

  • Chengdong Li
    • 1
    Email author
  • Weina Ren
    • 1
  • Jianqiang Yi
    • 2
  • Guiqing Zhang
    • 1
  • Fang Shang
    • 1
  1. 1.School of Information and Electrical EngineeringShandong Jianzhu UniversityJinanChina
  2. 2.Institute of AutomationChinese Academy of SciencesBeijingChina

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