An ELM-based single input rule module and its application in power generation

Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. In traditional fuzzy inference method which was the "if-then" rules, all the input and output objects were as...

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Main Authors: Yaw C.T., Wong S.Y., Yap K.S.
Other Authors: 36560884300
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Published: Institute of Advanced Engineering and Science 2023
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Institution: Universiti Tenaga Nasional
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spelling my.uniten.dspace-255482023-05-29T16:10:44Z An ELM-based single input rule module and its application in power generation Yaw C.T. Wong S.Y. Yap K.S. 36560884300 55812054100 24448864400 Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. In traditional fuzzy inference method which was the "if-then" rules, all the input and output objects were assigned to antecedent and consequent component respectively. However, a major dilemma was that the fuzzy rules' number kept increasing until the system and arrangement of the rules became complicated. Therefore, the single input rule modules connected type fuzzy inference (SIRM) method where consociated the output of the fuzzy rules modules significantly. In this paper, we put forward a novel single input rule modules based on extreme learning machine (denoted as SIRM-ELM) for solving data regression problems. In this hybrid model, the concept of SIRM is applied as hidden neurons of ELM and each of them represents a single input fuzzy rules. Hence, the number of fuzzy rule and the number of hidden neuron of ELM are the same. The effectiveness of proposed SIRM-ELM model is verified using sigmoid activation functions based on several benchmark datasets and a NOx emission of power generation plant. Experimental results illustrate that our proposed SIRM-ELM model is capable of achieving small root mean square error, i.e., 0.027448 for prediction of NOx emission. � 2020, Institute of Advanced Engineering and Science. All rights reserved. Final 2023-05-29T08:10:43Z 2023-05-29T08:10:43Z 2020 Article 10.11591/ijpeds.v11.i1.pp359-366 2-s2.0-85090618994 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090618994&doi=10.11591%2fijpeds.v11.i1.pp359-366&partnerID=40&md5=58eab1c248947ad3905c7717484c194d https://irepository.uniten.edu.my/handle/123456789/25548 11 1 359 366 All Open Access, Gold, Green Institute of Advanced Engineering and Science Scopus
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description Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. In traditional fuzzy inference method which was the "if-then" rules, all the input and output objects were assigned to antecedent and consequent component respectively. However, a major dilemma was that the fuzzy rules' number kept increasing until the system and arrangement of the rules became complicated. Therefore, the single input rule modules connected type fuzzy inference (SIRM) method where consociated the output of the fuzzy rules modules significantly. In this paper, we put forward a novel single input rule modules based on extreme learning machine (denoted as SIRM-ELM) for solving data regression problems. In this hybrid model, the concept of SIRM is applied as hidden neurons of ELM and each of them represents a single input fuzzy rules. Hence, the number of fuzzy rule and the number of hidden neuron of ELM are the same. The effectiveness of proposed SIRM-ELM model is verified using sigmoid activation functions based on several benchmark datasets and a NOx emission of power generation plant. Experimental results illustrate that our proposed SIRM-ELM model is capable of achieving small root mean square error, i.e., 0.027448 for prediction of NOx emission. � 2020, Institute of Advanced Engineering and Science. All rights reserved.
author2 36560884300
author_facet 36560884300
Yaw C.T.
Wong S.Y.
Yap K.S.
format Article
author Yaw C.T.
Wong S.Y.
Yap K.S.
spellingShingle Yaw C.T.
Wong S.Y.
Yap K.S.
An ELM-based single input rule module and its application in power generation
author_sort Yaw C.T.
title An ELM-based single input rule module and its application in power generation
title_short An ELM-based single input rule module and its application in power generation
title_full An ELM-based single input rule module and its application in power generation
title_fullStr An ELM-based single input rule module and its application in power generation
title_full_unstemmed An ELM-based single input rule module and its application in power generation
title_sort elm-based single input rule module and its application in power generation
publisher Institute of Advanced Engineering and Science
publishDate 2023
_version_ 1806424370035294208