Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151676
Title: Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system
Authors: Anh, Nguyen
Suresh, Sundaram
Pratama, Mahardhika
Srikanth, Narasimalu
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: 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
Journal: Knowledge-Based Systems
Abstract: 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.
URI: https://hdl.handle.net/10356/151676
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2019.01.025
Rights: © 2019 Published by Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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