Consensus Learning for Distributed Fuzzy Neural Network in Big Data Environment

Shi, Y; Lin, CT; Chang, YC; Ding, W; Shi, Y; Yao, X

Consensus Learning for Distributed Fuzzy Neural Network in Big Data Environment

Shi, Y

Lin, CT Chang, YC Ding, W Shi, Y

Yao, X

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Emerging Topics in Computational Intelligence, 2020, pp. 1-13
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Shi, Y https://orcid.org/0000-0003-2302-4980
dc.contributor.author	Lin, CT
dc.contributor.author	Chang, YC
dc.contributor.author	Ding, W
dc.contributor.author	Shi, Y https://orcid.org/0000-0003-2302-4980
dc.contributor.author	Yao, X
dc.date.accessioned	2020-07-23T00:54:34Z
dc.date.available	2020-07-23T00:54:34Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Transactions on Emerging Topics in Computational Intelligence, 2020, pp. 1-13
dc.identifier.issn	2471-285X
dc.identifier.uri	http://hdl.handle.net/10453/141841
dc.description.abstract	IEEE Uncertainty and distributed nature inherently exist in big data environment. Distributed fuzzy neural network (D-FNN) that not only employs fuzzy logics to alleviate the uncertainty problem but also deal with data in a distributed manner, is effective and crucial for big data. Existing D-FNNs always avoided consensus for their antecedent layer due to computational difficulty. Hence such D-FNNs are not really distributed since a single model can not be agreed by multiple agents. This article proposes a true D-FNN model to handle the uncertainty and distributed challenges in the big data environment. The proposed D-FNN model considers consensus for both the antecedent and consequent layers. A novel consensus learning, which involves a distributed structure learning and a distributed parameter learning, is proposed to handle the D-FNN model. The proposed consensus learning algorithm is built on the well-known alternating direction method of multipliers, which does not exchange local data among agents. The major contribution of this paper is to propose the true D-FNN model for the big data and the novel consensus learning algorithm for this D-FNN model. Simulation results on popular datasets demonstrate the superiority and effectiveness of the proposed D-FNN model and consensus learning algorithm.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation.ispartof	IEEE Transactions on Emerging Topics in Computational Intelligence
dc.relation.isbasedon	10.1109/TETCI.2020.2998919
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Consensus Learning for Distributed Fuzzy Neural Network in Big Data Environment
dc.type	Journal Article
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-06-09T00:00:00+1000Z
dc.date.updated	2020-07-23T00:54:29Z
pubs.publication-status	Published

Abstract:

IEEE Uncertainty and distributed nature inherently exist in big data environment. Distributed fuzzy neural network (D-FNN) that not only employs fuzzy logics to alleviate the uncertainty problem but also deal with data in a distributed manner, is effective and crucial for big data. Existing D-FNNs always avoided consensus for their antecedent layer due to computational difficulty. Hence such D-FNNs are not really distributed since a single model can not be agreed by multiple agents. This article proposes a true D-FNN model to handle the uncertainty and distributed challenges in the big data environment. The proposed D-FNN model considers consensus for both the antecedent and consequent layers. A novel consensus learning, which involves a distributed structure learning and a distributed parameter learning, is proposed to handle the D-FNN model. The proposed consensus learning algorithm is built on the well-known alternating direction method of multipliers, which does not exchange local data among agents. The major contribution of this paper is to propose the true D-FNN model for the big data and the novel consensus learning algorithm for this D-FNN model. Simulation results on popular datasets demonstrate the superiority and effectiveness of the proposed D-FNN model and consensus learning algorithm.

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http://hdl.handle.net/10453/141841