Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system
While significant efforts for online learning have been devoted to arrive at reliable predictions of crisp values, the problem of prediction interval (PI) in practical data is one of the underexplored areas in the existing literature. PI aims to produce upper and lower bound predictions which captur...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/10356/151676 |
| _version_ | 1811683232380354560 |
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| author | Anh, Nguyen Suresh, Sundaram Pratama, Mahardhika Srikanth, Narasimalu |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Anh, Nguyen Suresh, Sundaram Pratama, Mahardhika Srikanth, Narasimalu |
| author_sort | Anh, Nguyen |
| collection | NTU |
| description | While significant efforts for online learning have been devoted to arrive at reliable predictions of crisp values, the problem of prediction interval (PI) in practical data is one of the underexplored areas in the existing literature. PI aims to produce upper and lower bound predictions which capture possible domain solution. This paper aims to extend a prominent meta-cognitive learning algorithm, namely meta-cognitive interval type-2 fuzzy inference system (McIT2FIS), to cope with the problem of prediction interval in real-time. McIT2FIS is constructed under interval type-2 fuzzy inference system and realizes the meta-cognitive learning theory featuring the basic three elements of human learning: what-to-learn, how-to-learn, when-to-learn. Unlike existing works in PI, McIT2FIS-PI works fully in the online mode and is capable of performing automatic knowledge acquisition from data streams. The efficacy of McIT2FIS-PI has been experimentally validated in a real-world wave characteristics prediction in Semakau Island, Singapore, where it is capable of delivering accurate short-term prediction intervals of wave parameters. The performance of McIT2FIS-PI is also compared with existing state-of-the-art fuzzy inference systems in benchmark problems where it attains competitive accuracy while retaining comparable complexity. |
| first_indexed | 2024-10-01T04:09:28Z |
| format | Journal Article |
| id | ntu-10356/151676 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:09:28Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1516762021-07-14T07:27:46Z Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system Anh, Nguyen Suresh, Sundaram Pratama, Mahardhika Srikanth, Narasimalu School of Computer Science and Engineering Interdisciplinary Graduate School (IGS) Energy Research Institute @ NTU (ERI@N) Engineering::Computer science and engineering Wave Forecasting Fuzzy Logic While significant efforts for online learning have been devoted to arrive at reliable predictions of crisp values, the problem of prediction interval (PI) in practical data is one of the underexplored areas in the existing literature. PI aims to produce upper and lower bound predictions which capture possible domain solution. This paper aims to extend a prominent meta-cognitive learning algorithm, namely meta-cognitive interval type-2 fuzzy inference system (McIT2FIS), to cope with the problem of prediction interval in real-time. McIT2FIS is constructed under interval type-2 fuzzy inference system and realizes the meta-cognitive learning theory featuring the basic three elements of human learning: what-to-learn, how-to-learn, when-to-learn. Unlike existing works in PI, McIT2FIS-PI works fully in the online mode and is capable of performing automatic knowledge acquisition from data streams. The efficacy of McIT2FIS-PI has been experimentally validated in a real-world wave characteristics prediction in Semakau Island, Singapore, where it is capable of delivering accurate short-term prediction intervals of wave parameters. The performance of McIT2FIS-PI is also compared with existing state-of-the-art fuzzy inference systems in benchmark problems where it attains competitive accuracy while retaining comparable complexity. Nanyang Technological University The present work was supported by Energy Research Institute @Nanyang Technological University, Interdisciplinary Graduate School, Singapore. 2021-07-14T07:27:46Z 2021-07-14T07:27:46Z 2019 Journal Article Anh, N., Suresh, S., Pratama, M. & Srikanth, N. (2019). Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system. Knowledge-Based Systems, 169, 28-38. https://dx.doi.org/10.1016/j.knosys.2019.01.025 0950-7051 https://hdl.handle.net/10356/151676 10.1016/j.knosys.2019.01.025 2-s2.0-85061046251 169 28 38 en Knowledge-Based Systems © 2019 Published by Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Wave Forecasting Fuzzy Logic Anh, Nguyen Suresh, Sundaram Pratama, Mahardhika Srikanth, Narasimalu Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system |
| title | Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system |
| title_full | Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system |
| title_fullStr | Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system |
| title_full_unstemmed | Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system |
| title_short | Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system |
| title_sort | interval prediction of wave energy characteristics using meta cognitive interval type 2 fuzzy inference system |
| topic | Engineering::Computer science and engineering Wave Forecasting Fuzzy Logic |
| url | https://hdl.handle.net/10356/151676 |
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