Identification of Cement Rotary Kiln in Noisy Condition using Takagi-Sugeno Neuro-fuzzy System
Cement rotary kiln is the main part of cement production process that have always attracted many researchers’ attention. But this complex nonlinear system has not been modeled efficiently which can make an appropriate performance specially in noisy condition. In this paper Takagi-Sugeno neuro-fuzzy...
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Shahrood University of Technology
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doaj-20062f180b94429bb4e7fe9ef14ee99e2020-11-25T02:38:25ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442019-07-017336737510.22044/jadm.2018.5295.16381183Identification of Cement Rotary Kiln in Noisy Condition using Takagi-Sugeno Neuro-fuzzy SystemN. Moradkhani0M. Teshnehlab1Electrical Engineering Department, K.N. Toosi University of Technology, Tehran, Iran.Electrical Engineering Department, K.N. Toosi University of Technology, Tehran, Iran.Cement rotary kiln is the main part of cement production process that have always attracted many researchers’ attention. But this complex nonlinear system has not been modeled efficiently which can make an appropriate performance specially in noisy condition. In this paper Takagi-Sugeno neuro-fuzzy system (TSNFS) is used for identification of cement rotary kiln, and gradient descent (GD) algorithm is applied for tuning the parameters of antecedent and consequent parts of fuzzy rules. In addition, the optimal inputs of the system are selected by genetic algorithm (GA) to achieve less complexity in fuzzy system. The data related to Saveh White Cement (SWC) factory is used in simulations. The Results demonstrate that the proposed identifier has a better performance in comparison with neural and fuzzy models have presented earlier for the same data. Furthermore, in this paper TSNFS is evaluated in noisy condition which had not been worked out before in related researches. Simulations show that this model has a proper performance in different noisy condition.http://jad.shahroodut.ac.ir/article_1183_2d32fd30d6e70e613bc412843d707256.pdfcement rotary kilntakagi-sugeno fuzzy systemfeatuer selectionnoisy condition |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
N. Moradkhani M. Teshnehlab |
spellingShingle |
N. Moradkhani M. Teshnehlab Identification of Cement Rotary Kiln in Noisy Condition using Takagi-Sugeno Neuro-fuzzy System Journal of Artificial Intelligence and Data Mining cement rotary kiln takagi-sugeno fuzzy system featuer selection noisy condition |
author_facet |
N. Moradkhani M. Teshnehlab |
author_sort |
N. Moradkhani |
title |
Identification of Cement Rotary Kiln in Noisy Condition using Takagi-Sugeno Neuro-fuzzy System |
title_short |
Identification of Cement Rotary Kiln in Noisy Condition using Takagi-Sugeno Neuro-fuzzy System |
title_full |
Identification of Cement Rotary Kiln in Noisy Condition using Takagi-Sugeno Neuro-fuzzy System |
title_fullStr |
Identification of Cement Rotary Kiln in Noisy Condition using Takagi-Sugeno Neuro-fuzzy System |
title_full_unstemmed |
Identification of Cement Rotary Kiln in Noisy Condition using Takagi-Sugeno Neuro-fuzzy System |
title_sort |
identification of cement rotary kiln in noisy condition using takagi-sugeno neuro-fuzzy system |
publisher |
Shahrood University of Technology |
series |
Journal of Artificial Intelligence and Data Mining |
issn |
2322-5211 2322-4444 |
publishDate |
2019-07-01 |
description |
Cement rotary kiln is the main part of cement production process that have always attracted many researchers’ attention. But this complex nonlinear system has not been modeled efficiently which can make an appropriate performance specially in noisy condition. In this paper Takagi-Sugeno neuro-fuzzy system (TSNFS) is used for identification of cement rotary kiln, and gradient descent (GD) algorithm is applied for tuning the parameters of antecedent and consequent parts of fuzzy rules. In addition, the optimal inputs of the system are selected by genetic algorithm (GA) to achieve less complexity in fuzzy system. The data related to Saveh White Cement (SWC) factory is used in simulations. The Results demonstrate that the proposed identifier has a better performance in comparison with neural and fuzzy models have presented earlier for the same data. Furthermore, in this paper TSNFS is evaluated in noisy condition which had not been worked out before in related researches. Simulations show that this model has a proper performance in different noisy condition. |
topic |
cement rotary kiln takagi-sugeno fuzzy system featuer selection noisy condition |
url |
http://jad.shahroodut.ac.ir/article_1183_2d32fd30d6e70e613bc412843d707256.pdf |
work_keys_str_mv |
AT nmoradkhani identificationofcementrotarykilninnoisyconditionusingtakagisugenoneurofuzzysystem AT mteshnehlab identificationofcementrotarykilninnoisyconditionusingtakagisugenoneurofuzzysystem |
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