Hybrid fuzzy wavelet neural networks architecture based on polynomial neural networks and fuzzy set/relation inference-based wavelet neurons

Huang, W; Oh, SK; Pedrycz, W

Hybrid fuzzy wavelet neural networks architecture based on polynomial neural networks and fuzzy set/relation inference-based wavelet neurons

Huang, W Oh, SK Pedrycz, W

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Networks and Learning Systems, 2018, 29 (8), pp. 3452 - 3462
Issue Date:: 2018-08-01

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Field	Value	Language
dc.contributor.author	Huang, W	en_US
dc.contributor.author	Oh, SK	en_US
dc.contributor.author	Pedrycz, W https://orcid.org/0000-0002-9335-9930	en_US
dc.date.issued	2018-08-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems, 2018, 29 (8), pp. 3452 - 3462	en_US
dc.identifier.issn	2162-237X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132030
dc.description.abstract	© 2012 IEEE. This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Networks and Learning Systems	en_US
dc.relation.isbasedon	10.1109/TNNLS.2017.2729589	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Hybrid fuzzy wavelet neural networks architecture based on polynomial neural networks and fuzzy set/relation inference-based wavelet neurons	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	29	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	29	en_US

Abstract:

© 2012 IEEE. This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.

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http://hdl.handle.net/10453/132030