Evaluation of randomized variable translation wavelet neural networks

Anam, K; Al-Jumaily, A

Evaluation of randomized variable translation wavelet neural networks

Anam, K

Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: Communications in Computer and Information Science, 2017, 788 pp. 3 - 12
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Anam, K https://orcid.org/0000-0003-4169-2237	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Communications in Computer and Information Science, 2017, 788 pp. 3 - 12	en_US
dc.identifier.isbn	9789811072413	en_US
dc.identifier.issn	1865-0929	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127675
dc.description.abstract	© Springer Nature Singapore Pte Ltd. 2017. A variable translation wavelet neural network (VT-WNN) is a type of wavelet neural network that is able to adapt to the changes in the input. Different learning algorithms have been proposed such as backpropagation and hybrid wavelet-particle swarm optimization. However, most of them are time costly. This paper proposed a new learning mechanism for VT-WNN using random weights. To validate the performance of randomized VT-WNN, several experiments using benchmark data form UCI machine learning datasets were conducted. The experimental results show that RVT-WNN can work on a broad range of applications from the small size up to the large size with comparable performance to other well-known classifiers.	en_US
dc.relation.ispartof	Communications in Computer and Information Science	en_US
dc.relation.isbasedon	10.1007/978-981-10-7242-0_1	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Evaluation of randomized variable translation wavelet neural networks	en_US
dc.type	Conference Proceeding
utslib.citation.volume	788	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	788	en_US

Abstract:

© Springer Nature Singapore Pte Ltd. 2017. A variable translation wavelet neural network (VT-WNN) is a type of wavelet neural network that is able to adapt to the changes in the input. Different learning algorithms have been proposed such as backpropagation and hybrid wavelet-particle swarm optimization. However, most of them are time costly. This paper proposed a new learning mechanism for VT-WNN using random weights. To validate the performance of randomized VT-WNN, several experiments using benchmark data form UCI machine learning datasets were conducted. The experimental results show that RVT-WNN can work on a broad range of applications from the small size up to the large size with comparable performance to other well-known classifiers.

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http://hdl.handle.net/10453/127675