On equivalence of FIS and ELM for interpretable rule-based knowledge representation
Classification (of information); Computer aided diagnosis; Fault detection; Fuzzy systems; Knowledge acquisition; Knowledge representation; Learning systems; Matrix algebra; Membership functions; Pattern recognition; Extreme learning machine; Fault detection and diagnosis; Fuzzy if-then rules; Fuzzy...
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2023
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my.uniten.dspace-223192023-05-29T14:00:11Z On equivalence of FIS and ELM for interpretable rule-based knowledge representation Wong S.Y. Yap K.S. Yap H.J. Tan S.C. Chang S.W. 55812054100 24448864400 35319362200 7403366395 55276259900 Classification (of information); Computer aided diagnosis; Fault detection; Fuzzy systems; Knowledge acquisition; Knowledge representation; Learning systems; Matrix algebra; Membership functions; Pattern recognition; Extreme learning machine; Fault detection and diagnosis; Fuzzy if-then rules; Fuzzy inference systems; Fuzzy membership function; Initialization technique; Interpretable rules; Rule based; Fuzzy inference; algorithm; artificial intelligence; artificial neural network; benchmarking; classification; electric power plant; factual database; feedback system; fuzzy logic; machine learning; nerve cell; reproducibility; statistical model; Algorithms; Artificial Intelligence; Benchmarking; Classification; Databases, Factual; Feedback; Fuzzy Logic; Machine Learning; Models, Statistical; Neural Networks (Computer); Neurons; Power Plants; Reproducibility of Results This paper presents a fuzzy extreme learning machine (F-ELM) that embeds fuzzy membership functions and rules into the hidden layer of extreme learning machine (ELM). Similar to the concept of ELM that employed the random initialization technique, three parameters of F-ELM are randomly assigned. They are the standard deviation of the membership functions, matrix-C (rule-combination matrix), and matrix-D [don't care (DC) matrix]. Fuzzy if-then rules are formulated by the rule-combination Matrix of F-ELM, and a DC approach is adopted to minimize the number of input attributes in the rules. Furthermore, F-ELM utilizes the output weights of the ELM to form the target class and confidence factor for each of the rules. This is to indicate that the corresponding consequent parameters are determined analytically. The operations of F-ELM are equivalent to a fuzzy inference system. Several benchmark data sets and a real world fault detection and diagnosis problem have been used to empirically evaluate the efficacy of the proposed F-ELM in handling pattern classification tasks. The results show that the accuracy rates of F-ELM are comparable (if not superior) to ELM with distinctive ability of providing explicit knowledge in the form of interpretable rule base. � 2012 IEEE. Final 2023-05-29T06:00:11Z 2023-05-29T06:00:11Z 2015 Article 10.1109/TNNLS.2014.2341655 2-s2.0-84933037890 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933037890&doi=10.1109%2fTNNLS.2014.2341655&partnerID=40&md5=c9853354cfb08f5b1ff0bef3592487cc https://irepository.uniten.edu.my/handle/123456789/22319 26 7 6877713 1417 1430 Institute of Electrical and Electronics Engineers Inc. Scopus |
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Classification (of information); Computer aided diagnosis; Fault detection; Fuzzy systems; Knowledge acquisition; Knowledge representation; Learning systems; Matrix algebra; Membership functions; Pattern recognition; Extreme learning machine; Fault detection and diagnosis; Fuzzy if-then rules; Fuzzy inference systems; Fuzzy membership function; Initialization technique; Interpretable rules; Rule based; Fuzzy inference; algorithm; artificial intelligence; artificial neural network; benchmarking; classification; electric power plant; factual database; feedback system; fuzzy logic; machine learning; nerve cell; reproducibility; statistical model; Algorithms; Artificial Intelligence; Benchmarking; Classification; Databases, Factual; Feedback; Fuzzy Logic; Machine Learning; Models, Statistical; Neural Networks (Computer); Neurons; Power Plants; Reproducibility of Results |
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55812054100 Wong S.Y. Yap K.S. Yap H.J. Tan S.C. Chang S.W. |
| format |
Article |
| author |
Wong S.Y. Yap K.S. Yap H.J. Tan S.C. Chang S.W. |
| spellingShingle |
Wong S.Y. Yap K.S. Yap H.J. Tan S.C. Chang S.W. On equivalence of FIS and ELM for interpretable rule-based knowledge representation |
| author_sort |
Wong S.Y. |
| title |
On equivalence of FIS and ELM for interpretable rule-based knowledge representation |
| title_short |
On equivalence of FIS and ELM for interpretable rule-based knowledge representation |
| title_full |
On equivalence of FIS and ELM for interpretable rule-based knowledge representation |
| title_fullStr |
On equivalence of FIS and ELM for interpretable rule-based knowledge representation |
| title_full_unstemmed |
On equivalence of FIS and ELM for interpretable rule-based knowledge representation |
| title_sort |
on equivalence of fis and elm for interpretable rule-based knowledge representation |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
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1806423236820336640 |