Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

Hasan ABBASI NOZARI; Hamed DEHGHAN BANADAKI; Mohammad MOKHTARE; Somayeh HEKMATI VAHED

doi:10.1631/jzus.C11a0278

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Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

计算机与自动化 | Updated：2022-12-09

- Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks
- Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks
- 信息与电子工程前沿（英文） 2012年13卷第6期页码：403-412
- Affiliations：
  
  Department of Mechatronics, Faculty of Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
- Author bio：
  
  [ "Hasan ABBASI NOZARI, E-mail: nozari.amirali@gmail.com" ]
  [ "Hamed DEHGHAN BANADAKI, E-mail: hmd.dehghan@gmail.com" ]
  [ "Mohammad MOKHTARE, E-mail: m.mokhtare@srbiau.ac.ir" ]
  [ "Somayeh HEKMATI VAHED, E-mail: s.hekmati@srbiau.ac.ir" ]
- Funds：
- DOI：10.1631/jzus.C11a0278
  中图分类号： TP183
- 收稿：2011-10-17，
  
  修回：2012-;4-;9，
  
  纸质出版：2012-06
- Accepted：
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Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks[J]. 信息与电子工程前沿（英文）, 2012,13(6):403-412.

Hasan ABBASI NOZARI, Hamed DEHGHAN BANADAKI, Mohammad MOKHTARE, et al. Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(6): 403-412.
Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks[J]. 信息与电子工程前沿（英文）, 2012,13(6):403-412. DOI： 10.1631/jzus.C11a0278.

Hasan ABBASI NOZARI, Hamed DEHGHAN BANADAKI, Mohammad MOKHTARE, et al. Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(6): 403-412. DOI： 10.1631/jzus.C11a0278.

摘要

Abstract

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT)

which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers

namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.

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references

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