A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

Quteishat, Anas; Lim, Chee Peng; Mohamad–Saleh, Junita; Tweedale, Jeffrey W.; Jain, Lakhmi C.

doi:10.1007/s00500-010-0592-0

Cited by 15 publications

(11 citation statements)

References 30 publications

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“…To attain the trust measurement, the FMM in the model was used as a learning agent in MAS and tailed by combination with Bayesian formalism. The results in proposed model showed improvement as compared to other tactics [91].…”

Section: (Case 3) Improved Hybridizing Elm Based Multi Agent Systementioning

confidence: 80%

“…Consequently, trust was the strong element related to the FMM agents that enabled the CBS technique to increase the performance of the MAS in the training practice. The result showed that the improvement of the accuracy rates of the individual agents [91].…”

Section: (Case 3) Improved Hybridizing Elm Based Multi Agent Systementioning

confidence: 94%

“…Better performances were reported in the model as compared to other tactics stated in. On the other hand, there was one more trust measurement tactic suggested which based on Bayesian formalism with FMM MAS [91]. To attain the trust measurement, the FMM in the model was used as a learning agent in MAS and tailed by combination with Bayesian formalism.…”

Section: (Case 3) Improved Hybridizing Elm Based Multi Agent Systementioning

confidence: 99%

“…A technique called Certified Belief in Strength (CBS) is the latest development of MAS model for trust measurement, which was based on the reputation and strength of individual FMM based agents [91]. Consequently, trust was the strong element related to the FMM agents that enabled the CBS technique to increase the performance of the MAS in the training practice.…”

Section: (Case 3) Improved Hybridizing Elm Based Multi Agent Systementioning

confidence: 99%

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Enhancement of Neural Network Based Multi Agent System for Classification and Regression in Energy System

et al. 2020

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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.

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Section: (Case 3) Improved Hybridizing Elm Based Multi Agent Systementioning

confidence: 80%

Section: (Case 3) Improved Hybridizing Elm Based Multi Agent Systementioning

confidence: 94%

Section: (Case 3) Improved Hybridizing Elm Based Multi Agent Systementioning

confidence: 99%

Section: (Case 3) Improved Hybridizing Elm Based Multi Agent Systementioning

confidence: 99%

See 2 more Smart Citations

Enhancement of Neural Network Based Multi Agent System for Classification and Regression in Energy System

et al. 2020

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show abstract

“…Each agent makes its own complete classification and the best one is chosen. Quteishat and his team have developed a Multi-Agent Classifier system based on the TrustNegotiation-Communication model (Quteishat & al, 2010). The proposed TNC-based MAC system consists of an ensemble of neural network-based classifiers.…”

Section: Related Work Similarities and Originalitymentioning

confidence: 99%