An ELM based multi-agent system and its applications to power generation

Chong Tak Yaw, Shen Yuong Wong, Keem Siah Yap, Hwa Jen Yap, Ungku Anisa Ungku Amirulddin Al Amin, Shing Chiang Tan

Research output: Contribution to journalArticle


This paper presents an implementation of Extreme Learning Machine (ELM) in the Multi-Agent System (MAS). The proposed method is a trust measurement approach namely Certified Belief in Strength (CBS) for Extreme Learning Machine in Multi-Agent Systems (ELM-MAS-CBS). The CBS is applied on the individual agents of MAS, i.e., ELM neural network. The trust measurement is introduced to compute reputation and strength of the individual agents. Strong elements that are related to the ELM agents are assembled to form the trust management in which will be letting the CBS method to improve the performance in MAS. The efficacy of the ELM-MAS-CBS model is verified with several activation functions using benchmark datasets (i.e., Pima Indians Diabetes, Iris andWine) and real world applications (i.e., circulating water systems and governor). The results show that the proposed ELM-MAS-CBS model is able to achieve better accuracy as compared with other approaches.

Original languageEnglish
Pages (from-to)297-305
Number of pages9
JournalIntelligent Decision Technologies
Issue number3
Publication statusPublished - 01 Jan 2017


All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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