Multiple neural control strategies using a neuro-fuzzy classifier

被引：1
作者：
Dehmani K. [1 ]
Fourati F. [2 ]
Elleuch K. [1 ]
Toumi A. [1 ]
机构：
[1] Laboratory Lab-STA, University of Sfax
[2] Control and Energy Management Laboratory (CEM-Lab), University of Sfax
来源：
Automatic Control and Computer Sciences | 2018年 / 52卷 / 3期
关键词：
Behaviors; Direct neural models; Greenhouse; Multiple neural controls; Neural controllers; Neuro-fuzzy classifier;
D O I：
10.3103/S0146411618030069
中图分类号：
学科分类号：
摘要：
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. © Allerton Press, Inc., 2018.
引用
收藏
页码：155 / 165
页数：10
相关论文
共 23 条
  • [1] Hahn F., Fuzzy controller decreases tomato cracking in greenhouses, Comput. Electron. Agric., 77, pp. 1-27, (2011)
  • [2] Nachidi M., Rodriguez F., Tadeo F., Guzman J.L., Takagi–Sugeno control of nocturnal temperature in greenhouses using air heating, ISA Trans, 50, pp. 315-320, (2011)
  • [3] Lafont F., Balmat J.F., Pessel N., Fliess M., A model-free control strategy for an experimental greenhouse with an application to fault accommodation, Comput. Electron. Agric., 110, pp. 139-149, (2015)
  • [4] Mohamed S., A GA-based adaptive neuro-fuzzy controller for greenhouse climate control system, Alexandria Eng. J., (2015)
  • [5] Tanougast M., Fabrizio E., Mami A., Smart greenhouse fuzzy logic based control system enhanced with wireless data monitoring, ISA Trans, 61, pp. 297-307, (2016)
  • [6] Fourati F., Chtourou M., A greenhouse control with feedforward and recurrent neural networks, Simul. Modell. Pract. Theor., 15, pp. 1016-1028, (2007)
  • [7] Xiaoli L., Peng S.F.L., Robust adaptive control for greenhouse climate using neural networks, Int. J. Robust Nonlinear Control, 21, pp. 815-826, (2011)
  • [8] Xiong Y., Cheng H., Shen M., He W., Liu Y., Zhao L., Sun Y., Hu X., Lu M., Wu J., Liu L., Zheng B., Design of intelligent greenhouse information management system with hybrid architecture, Trans. Chin. Soc. Agric. Eng., 28, pp. 181-185, (2012)
  • [9] Shi X.Y., Ye H.B., Li D., Xu Z.F., Development and trend of intelligent monitoring system for greenhouse, Adv. Mat. Res., 1030-1032, pp. 1475-1479, (2014)
  • [10] Ghosh S., Biswas S., Sarka D., Sarkar P.P., A novel neuro-fuzzy classification technique for data mining, Egypt. Inf. J., 15, pp. 129-147, (2014)
123