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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (6): 403-412    DOI: 10.1631/jzus.C11a0278
    
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
Department of Mechatronics, Faculty of Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
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
Department of Mechatronics, Faculty of Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
 全文: PDF 
摘要： 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.
关键词： Non-linear system identification;  Recurrent local linear neuro-fuzzy (RLLNF) network;  Local linear model tree (LOLIMOT);  Neural network (NN);  Industrial winding process    
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.
Key words: Non-linear system identification    Recurrent local linear neuro-fuzzy (RLLNF) network    Local linear model tree (LOLIMOT)    Neural network (NN)    Industrial winding process
收稿日期: 2011-10-17 出版日期: 2012-06-05
CLC:  TP183  
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Hasan Abbasi Nozari
Hamed Dehghan Banadaki
Mohammad Mokhtare
Somayeh Hekmati Vahed

引用本文:

Hasan Abbasi Nozari, Hamed Dehghan Banadaki, Mohammad Mokhtare, Somayeh Hekmati Vahed. Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks. Front. Inform. Technol. Electron. Eng., 2012, 13(6): 403-412.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C11a0278    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I6/403

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