Enhancement of neural network based multi agent system for classification and regression in energy system

Yaw, Chong Tak and Yap, Keem Siah and Wong, Shen Yuong and Yap, Hwa Jen and Paw, Johnny Koh Siew (2020) Enhancement of neural network based multi agent system for classification and regression in energy system. IEEE Access, 8. pp. 163026-163043. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2020.3012983.

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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.

Item Type: Article
Funders: Universiti Tenaga Nasional (10436494/B/2019099), Xiamen University Malaysia Research Fund (XMUMRF/2018-C2/IECE/0001)
Uncontrolled Keywords: Biological neural networks; Neurons; Training; Indexes; Pattern recognition; Pattern classification; Power generation; Certified belief in strength; extreme learning machine; multi agent system; single input rule module; power generation system
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering > Department of Mechanical Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 15 Mar 2023 02:41
Last Modified: 15 Mar 2023 02:41
URI: http://eprints.um.edu.my/id/eprint/37206

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