research-article

Optimization of RBFneural network used in state recognition of coal flotation

Authors: Tian Muling, Tian Muqin, Yang Jieming, Julie LiAuthors Info & Claims

Journal of Intelligent & Fuzzy Systems, Volume 34, Issue 2

Pages 1193 - 1204

https://doi.org/10.3233/JIFS-169414

Published: 01 January 2018 Publication History

Abstract

In order to make the RBF hidden layer centres being established more adaptively and avoid the blindness, this paper proposes a fusion algorithm in order to optimize the parameters of the RBF neural network used in recognizing the state of coal flotation. Firstly, in the optimization algorithm, the improved immune algorithm was used to determine the center position and the number of hidden layer of RBF neural network. Before this, the immune algorithm has been improved in several aspects, such as the initial population selection algorithm and the method for segment selection of affinity thresholds. In addition, the antibody removal mechanism, antibody immune mechanism and antibody concentration regulation principle had also been added in immune algorithm. Secondly, in virtue of combining a fuzzy C-means clustering algorithm, the centers of the hidden layer were optimized accurately. Through the sample verification, the RBF neural network obtained by the fusion algorithm was proved to have been improved significantly in the accuracy of identifying the coal flotation state and has better generalization ability.

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Cited By

Li YJia XWang RQi JJin HChu XMu W(2022)A new oversampling method and improved radial basis function classifier for customer consumption behavior predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116982199:COnline publication date: 23-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116982

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Optimization of RBFneural network used in state recognition of coal flotation

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cover image Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology Volume 34, Issue 2

Artificial Intelligent Techniques and its Applications

2018

460 pages

ISSN:1064-1246

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© 2018 – IOS Press and the authors. All rights reserved.

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IOS Press

Netherlands

Publication History

Published: 01 January 2018

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Cited By

Li YJia XWang RQi JJin HChu XMu W(2022)A new oversampling method and improved radial basis function classifier for customer consumption behavior predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116982199:COnline publication date: 23-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116982

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