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Multiple Neural Control Strategies Using a Neuro-Fuzzy Classifier

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Abstract

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.

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Authors and Affiliations

  1. Laboratory Lab-STA, Sfax, Tunisia

    Khaled Dehmani, Khaled Elleuch & Ahmed Toumi

  2. Control and Energy Management Laboratory (CEM-Lab), Sfax, Tunisia

    Fathi Fourati

Authors
  1. Khaled Dehmani
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  2. Fathi Fourati
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  3. Khaled Elleuch
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  4. Ahmed Toumi
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Correspondence to Fathi Fourati.

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Dehmani, K., Fourati, F., Elleuch, K. et al. Multiple Neural Control Strategies Using a Neuro-Fuzzy Classifier. Aut. Control Comp. Sci. 52, 155–165 (2018). https://doi.org/10.3103/S0146411618030069

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  • DOI: https://doi.org/10.3103/S0146411618030069

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