Wave Forecasting using Meta-cognitive Interval Type-2 Fuzzy Inference System

Anh, N; Prasad, M; Srikanth, N; Sundaram, S

Wave Forecasting using Meta-cognitive Interval Type-2 Fuzzy Inference System

Anh, N Prasad, M

Srikanth, N Sundaram, S

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Publication Type:: Conference Proceeding
Citation:: Procedia Computer Science, 2018, 144 pp. 33 - 41
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Anh, N	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.contributor.author	Srikanth, N	en_US
dc.contributor.author	Sundaram, S	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Procedia Computer Science, 2018, 144 pp. 33 - 41	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131692
dc.description.abstract	© 2018 The Authors. Published by Elsevier Ltd. Renewable energy is fast becoming a mainstay in today's energy scenario. One of the important sources of renewable energy is the wave energy, in addition to wind, solar, tidal, etc. Wave prediction/forecasting is consequently essential in coastal and ocean engineering studies. However, it is difficult to predict wave parameters in long term and even in the short term due to its intermittent nature. This study aims to propose a solution to handle the issue using Interval type-2 fuzzy inference system, or IT2FIS. IT2FIS has been shown to be capable of handling uncertainty associated with the data. The proposed IT2FIS is a fuzzy neural network realizing Takagi-Sugeno-Kang inference mechanism employing meta-cognitive learning algorithm. The algorithm monitors knowledge in a sample to decide an appropriate learning strategy. Performance of the system is evaluated by studying significant wave heights obtained from buoys located in Singapore. The results compared with existing state-of-the art fuzzy inference system approaches clearly indicate the advantage of IT2FIS based wave prediction.	en_US
dc.relation.ispartof	Procedia Computer Science	en_US
dc.relation.isbasedon	10.1016/j.procs.2018.10.502	en_US
dc.title	Wave Forecasting using Meta-cognitive Interval Type-2 Fuzzy Inference System	en_US
dc.type	Conference Proceeding
utslib.citation.volume	144	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	10 Technology	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	144	en_US

Abstract:

© 2018 The Authors. Published by Elsevier Ltd. Renewable energy is fast becoming a mainstay in today's energy scenario. One of the important sources of renewable energy is the wave energy, in addition to wind, solar, tidal, etc. Wave prediction/forecasting is consequently essential in coastal and ocean engineering studies. However, it is difficult to predict wave parameters in long term and even in the short term due to its intermittent nature. This study aims to propose a solution to handle the issue using Interval type-2 fuzzy inference system, or IT2FIS. IT2FIS has been shown to be capable of handling uncertainty associated with the data. The proposed IT2FIS is a fuzzy neural network realizing Takagi-Sugeno-Kang inference mechanism employing meta-cognitive learning algorithm. The algorithm monitors knowledge in a sample to decide an appropriate learning strategy. Performance of the system is evaluated by studying significant wave heights obtained from buoys located in Singapore. The results compared with existing state-of-the art fuzzy inference system approaches clearly indicate the advantage of IT2FIS based wave prediction.

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http://hdl.handle.net/10453/131692