Enhancement of neural network based multi agent system for classification and regression in energy system
Electric circuit breakers; Fuzzy inference; Hybrid systems; Iterative methods; Knowledge acquisition; Learning systems; Multi agent systems; Support vector machines; Benchmark datasets; Circulating water system; Extreme learning machine; ITS applications; Network structures; Power generation systems...
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.uniten.dspace-256222023-05-29T16:11:49Z Enhancement of neural network based multi agent system for classification and regression in energy system Yaw C.T. Yap K.S. Wong S.Y. Yap H.J. Paw J.K.S. 36560884300 24448864400 55812054100 35319362200 57224774999 Electric circuit breakers; Fuzzy inference; Hybrid systems; Iterative methods; Knowledge acquisition; Learning systems; Multi agent systems; Support vector machines; Benchmark datasets; Circulating water system; Extreme learning machine; ITS applications; Network structures; Power generation systems; Trust management; Trust measurement; Neural networks Extreme Learning Machine improved the iterative procedures of adjusting weights by randomly selecting hidden neurons besides analytically determining the output weights. In this paper, the basic ELM neural network was enhanced with a simplified network structure to achieve regression performance. Next, to solve the pattern classification, a hybrid system was proposed which integrated the ELM neural network and MAS models. A MAS model is then designed with a novel trust measurement method to combine ELM neural networks. Firstly, ELM hybrid with Single Input Rule Module (SIRM-ELM) was designed. There was only a single input connected to the rules, where the rules were the hidden neurons of ELM and each represented a single input fuzzy rules. Results showed that the SIRM-ELM model was better than Support Vector Machine and traditional ELM. Secondly, an extreme learning machine based multi agent systems (ELM-MAS) was designed to improve ELM's capability. Its first layer was made up of at least one ELM where ELM acted as an individual agent, whereas another layer was made up of a single ELM acting as the parent agent. Lastly, Certified Belief in Strength (CBS) method was applied to the ELM neural network to form ELM-MAS-CBS, using the reputation and strength of individual agents as the trust measurement. The assembly of strong elements related to the ELM agents formed the trust management that allowed the improvement of the performance in MAS using the CBS method. Both of the developed models were evaluated on its application on the power generation system. The test accuracy rate of both models for circulating water systems was shown to be comparable to other algorithms. In short, the developed models had been verified using benchmark datasets and applied in power generation, where the results were satisfactory. � 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved. Final 2023-05-29T08:11:49Z 2023-05-29T08:11:49Z 2020 Article 10.1109/ACCESS.2020.3012983 2-s2.0-85102884215 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102884215&doi=10.1109%2fACCESS.2020.3012983&partnerID=40&md5=5b965220af6090132740d4b87709cff9 https://irepository.uniten.edu.my/handle/123456789/25622 8 163026 163043 All Open Access, Gold Institute of Electrical and Electronics Engineers Inc. Scopus |
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| description |
Electric circuit breakers; Fuzzy inference; Hybrid systems; Iterative methods; Knowledge acquisition; Learning systems; Multi agent systems; Support vector machines; Benchmark datasets; Circulating water system; Extreme learning machine; ITS applications; Network structures; Power generation systems; Trust management; Trust measurement; Neural networks |
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36560884300 |
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36560884300 Yaw C.T. Yap K.S. Wong S.Y. Yap H.J. Paw J.K.S. |
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Article |
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Yaw C.T. Yap K.S. Wong S.Y. Yap H.J. Paw J.K.S. |
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Yaw C.T. Yap K.S. Wong S.Y. Yap H.J. Paw J.K.S. Enhancement of neural network based multi agent system for classification and regression in energy system |
| author_sort |
Yaw C.T. |
| title |
Enhancement of neural network based multi agent system for classification and regression in energy system |
| title_short |
Enhancement of neural network based multi agent system for classification and regression in energy system |
| title_full |
Enhancement of neural network based multi agent system for classification and regression in energy system |
| title_fullStr |
Enhancement of neural network based multi agent system for classification and regression in energy system |
| title_full_unstemmed |
Enhancement of neural network based multi agent system for classification and regression in energy system |
| title_sort |
enhancement of neural network based multi agent system for classification and regression in energy system |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806428130252947456 |
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13.214268 |