Identification of Multiple Input-multiple Output Non-linear System Cement Rotary Kiln using Stochastic Gradient-based Rough-neural Network

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different purposes such as prediction, fault detection, and control. In the previous works, CRK was identified after decomposing it into several multiple input-single output (MISO) systems. In this paper, for the first time, the rough-neural network (R-NN) is utilized for the identification of CRK without the usage of MISO structures. R-NN is a neural structure designed on the base of rough set theory for dealing with the uncertainty and vagueness. In addition, a stochastic gradient descent learning algorithm is proposed for training the R-NNs. The simulation results show the effectiveness of proposed methodology.

Language:
English
Published:
Journal of Artificial Intelligence and Data Mining, Volume:8 Issue: 3, Summer 2020
Pages:
417 to 425
https://www.magiran.com/p2158132  
سامانه نویسندگان
  • Corresponding Author (1)
    Ghasem Ahmadi
    Assistant Professor Mathematics, Payame Noor University, Tehran, Iran
    Ahmadi، Ghasem
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