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 and Wine) 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.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Artificial Intelligence