Topology Learning-based Fuzzy Random Neural Network for Streaming Data Regression

Yu, H; Lu, J; Zhang, G

Topology Learning-based Fuzzy Random Neural Network for Streaming Data Regression

Yu, H

Lu, J

Zhang, G

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2022, 30, (2), pp. 412-425
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Yu, H https://orcid.org/0000-0003-3444-9992
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.date.accessioned	2023-02-27T04:03:40Z
dc.date.available	2023-02-27T04:03:40Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2022, 30, (2), pp. 412-425
dc.identifier.issn	1063-6706
dc.identifier.issn	1941-0034
dc.identifier.uri	http://hdl.handle.net/10453/166443
dc.description.abstract	IEEE As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural networks. However, for streaming data regression, EFN systems still have several drawbacks: 1) determining fuzzy sets is not robust to data sequence; 2) determining fuzzy rules is complex due to subspaces that can approximate to a Takagi-Sugeno-Kang (TSK) rule need to be obtained, and many parameters need to be optimized; 3) it is difficult to detect and adapt to changes in the data distribution, i.e., concept drift, if the output is a continuous variable. Hence, in this paper, a novel evolving-fuzzy-neuro system, called the topology learning-based fuzzy random neural network (TLFRNN), is proposed. In TLFRNN, an online topology learning algorithm is designed to self-organize each layer of TLFRNN. Different from current EFN systems, TLFRNN learns multiple fuzzy sets to reduce the impact of noises on each fuzzy set, and a randomness layer is designed, which assigning the probability of each fuzzy set. Also, TLFRNN does not utilize TSK rules instead uses a simple inference which considering fuzzy and random information of data simultaneously. More importantly, in TLFRNN, concept drift can be detected and adapted easily and rapidly. The experiments demonstrate that TLFRNN achieves superior performance compared to other EFSs.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DP190101733
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems
dc.relation.isbasedon	10.1109/TFUZZ.2020.3039681
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Topology Learning-based Fuzzy Random Neural Network for Streaming Data Regression
dc.type	Journal Article
utslib.citation.volume	30
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-02-27T04:03:07Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	2

Abstract:

IEEE As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural networks. However, for streaming data regression, EFN systems still have several drawbacks: 1) determining fuzzy sets is not robust to data sequence; 2) determining fuzzy rules is complex due to subspaces that can approximate to a Takagi-Sugeno-Kang (TSK) rule need to be obtained, and many parameters need to be optimized; 3) it is difficult to detect and adapt to changes in the data distribution, i.e., concept drift, if the output is a continuous variable. Hence, in this paper, a novel evolving-fuzzy-neuro system, called the topology learning-based fuzzy random neural network (TLFRNN), is proposed. In TLFRNN, an online topology learning algorithm is designed to self-organize each layer of TLFRNN. Different from current EFN systems, TLFRNN learns multiple fuzzy sets to reduce the impact of noises on each fuzzy set, and a randomness layer is designed, which assigning the probability of each fuzzy set. Also, TLFRNN does not utilize TSK rules instead uses a simple inference which considering fuzzy and random information of data simultaneously. More importantly, in TLFRNN, concept drift can be detected and adapted easily and rapidly. The experiments demonstrate that TLFRNN achieves superior performance compared to other EFSs.

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http://hdl.handle.net/10453/166443