Automatic Control and Computer Sciences

, Volume 52, Issue 3, pp 155–165 | Cite as

Multiple Neural Control Strategies Using a Neuro-Fuzzy Classifier

  • Khaled Dehmani
  • Fathi FouratiEmail author
  • Khaled Elleuch
  • Ahmed Toumi


The paper deals with the control of complex dynamic systems. The main objective is to partition the whole operational system domain in local regions using an incremental neuro-fuzzy classifier in order to achieve multiple neural control strategies for the considered system. In our case, this approach is applied to a greenhouse operating during one day. Therefore, banks of neural controllers and direct neural local models are made from different partitioned greenhouse behaviors and two multiple neural control strategies are proposed to control the greenhouse. The selection of the suitable controller is accomplished by computing the minimal output error between desired and direct neural local models outputs in the case of the first control strategy and from a supervisor block containing the considered neuro-fuzzy classifier in the case of the second control strategy. Simulation results are then carried out to show the efficiency of the two control strategies.


greenhouse neuro-fuzzy classifier behaviors neural controllers direct neural models multiple neural controls 


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  1. 1.
    Hahn, F., Fuzzy controller decreases tomato cracking in greenhouses, Comput. Electron. Agric., 2011, vol. 77, pp. 1–27.CrossRefGoogle Scholar
  2. 2.
    Nachidi, M., Rodriguez, F., Tadeo, F., and Guzman, J.L., Takagi–Sugeno control of nocturnal temperature in greenhouses using air heating, ISA Trans., 2011, vol. 50, pp. 315–320.CrossRefGoogle Scholar
  3. 3.
    Lafont, F., Balmat, J.F., Pessel, N., and Fliess, M., A model-free control strategy for an experimental greenhouse with an application to fault accommodation, Comput. Electron. Agric., 2015, vol. 110, pp. 139–149.CrossRefGoogle Scholar
  4. 4.
    Mohamed, S., A GA-based adaptive neuro-fuzzy controller for greenhouse climate control system, Alexandria Eng. J., 2015 (in press).Google Scholar
  5. 5.
    Tanougast, M., Fabrizio, E., and Mami, A., Smart greenhouse fuzzy logic based control system enhanced with wireless data monitoring, ISA Trans., 2016, vol. 61, pp. 297–307.CrossRefGoogle Scholar
  6. 6.
    Fourati, F. and Chtourou, M., A greenhouse control with feedforward and recurrent neural networks, Simul. Modell. Pract. Theor., 2007, vol. 15, pp. 1016–1028.CrossRefGoogle Scholar
  7. 7.
    Xiaoli, L. and Peng, S.F.L., Robust adaptive control for greenhouse climate using neural networks, Int. J. Robust Nonlinear Control, 2011, vol. 21, pp. 815–826.MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Xiong, Y., Cheng, H., Shen, M., He, W., Liu, Y., Zhao, L., Sun, Y., Hu, X., Lu, M., Wu, J., Liu, L., and Zheng, B., Design of intelligent greenhouse information management system with hybrid architecture, Trans. Chin. Soc. Agric. Eng., 2012, vol. 28, pp. 181–185.Google Scholar
  9. 9.
    Shi, X.Y., Ye, H.B., Li, D., and Xu, Z.F., Development and trend of intelligent monitoring system for greenhouse, Adv. Mat. Res., 2014, vols. 1030–1032, pp. 1475–1479.Google Scholar
  10. 10.
    Ghosh, S., Biswas, S., Sarka, D., and Sarkar, P.P., A novel neuro-fuzzy classification technique for data mining, Egypt. Inf. J., 2014, vol. 15, pp. 129–147.CrossRefGoogle Scholar
  11. 11.
    Cho, K.B. and Wang, B.H., Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction, Fuzzy Sets Syst., 1996, vol. 83, pp. 325–339.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Yang, M.S. and Wu, K.L., A similarity-based robust clustering method, IEEE Trans. Pattern Anal. Mach. Intell., 2004, vol. 26, pp. 434–448.CrossRefGoogle Scholar
  13. 13.
    Runkler, T.A. and Bezdek, J.C., Alternating cluster estimation: A new tool for clustering and function approximation, IEEE Trans. Fuzzy Syst., 1999, vol. 7, pp. 377–393.CrossRefGoogle Scholar
  14. 14.
    Elman, J.L., Finding structure in time, Cognit. Sci., 1990, vol. 14, pp. 179–211.CrossRefGoogle Scholar
  15. 15.
    Pham, D.T. and Liu, X., Neural Networks for Identification, Prediction and Control, London: Springer-Verlag, 1995.CrossRefGoogle Scholar
  16. 16.
    Hornik, K., Stinchcombe, M., and White, H., Multilayer feedforward networks are universal approximators, Neural Networks, 1989, vol. 2, pp. 359–366.CrossRefzbMATHGoogle Scholar
  17. 17.
    Psaltis, D., Sideris, A., and Yamamura, A.A., A multilayer neural network controller, IEEE Control Syst. Mag., 1988, vol. 8, pp. 17–21.CrossRefGoogle Scholar
  18. 18.
    Hunt, K.J., Sbarbaro, D., Zbikowski, R., and Gawthrop, P.J., Neural networks for control systems—a survey, Automatica, 1992, vol. 28, pp. 1083–1112.MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Narendra, K.S. and Parthasarathy, K., Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Networks, 1990, vol. 1, pp. 4–27.CrossRefGoogle Scholar
  20. 20.
    Jordan, M.I. and Rumelhart, D.E., Forward models: Supervised learning with a distal teacher, Cognit. Sci., 1992, vol. 16, pp. 307–354.CrossRefGoogle Scholar
  21. 21.
    Fourati, F. and Chtourou, M., A greenhouse neural control using generalized and specialized learning, Int. J. Innovative Comput. Inf. Control, 2011, vol. 7, pp. 1349–4198.Google Scholar
  22. 22.
    Fourati, F., Multiple neural control of a greenhouse, Neurocomputing, 2014, vol. 139, pp. 138–144.CrossRefGoogle Scholar
  23. 23.
    Fourati, F., Dehmani, K., and Elleuch, K., A greenhouse comportments extraction using a neuro-fuzzy classifier, 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA’2016, Sousse, 2016, pp. 429–433.Google Scholar

Copyright information

© Allerton Press, Inc. 2018

Authors and Affiliations

  • Khaled Dehmani
    • 1
  • Fathi Fourati
    • 2
    Email author
  • Khaled Elleuch
    • 1
  • Ahmed Toumi
    • 1
  1. 1.Laboratory Lab-STASfaxTunisia
  2. 2.Control and Energy Management Laboratory (CEM-Lab)SfaxTunisia

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