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首页> 外文期刊>Journal of computational and theoretical nanoscience >Fuzzy Cognitive Map Approach for Trust-Based Partner Selection in Virtual Enterprise
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Fuzzy Cognitive Map Approach for Trust-Based Partner Selection in Virtual Enterprise

机译：虚拟企业基于信任合作伙伴选择的模糊认知地图方法

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摘要

Minimizing risk in partner selection are the key problems to overcome in VE, in order to ensure success. Due to the dynamic nature of these collaborations, exploitation of trust is essential to support the formation of VEs. There is critical need for novel approaches suited to this environment for partner selection and coordination among them. However, few efficient models for partner selection are introduced yet to consider trustworthy as target criteria. This paper presents a trustbased approach for partner selection in VEs. The proposed model explores the dynamic properties of trustworthy index, which may provide a multi-perspective and interactive overview of potential partners to the decision-makers. The model uses a tool of the hybrid learning algorithm for Fuzzy Cognitive Map (FCM). It is combined of the real-coded genetic (RCGA) algorithm and nonlinear Hebbian learning (NHL) algorithm. By virtue of FCM's excellent structure adaptation capability in modeling complex systems, the proposed model will address the problem of enormous and multidimensional information, taken into consideration while making decisions. The proposed model has been validated by a set of experiments. Results show that our model provides reasonable performance and high adaptability for fast data mining and searching the optimal solution.
机译：最大限度地减少合作伙伴选择的风险是在VE中克服的关键问题，以确保成功。由于这些合作的动态性质，对信任的开发对于支持ves的形成至关重要。对于合适的伴侣选择和协调，对这种环境的新方法有关。但是，尚未为伙伴选择的少数有效的模型尚未考虑可信赖的目标标准。本文介绍了VES中合作伙伴选择的信任方法。拟议的模型探讨了值得信赖的指数的动态属性，这可能提供对决策者的多视角和互动概述潜在的合作伙伴。该模型使用混合学习算法的模糊认知地图（FCM）的工具。它是基于实际编码遗传（RCGA）算法和非线性HEBBIAN学习（NHL）算法的组合。凭借FCM在建模复杂系统中的优异结构适应能力方面，所提出的模型将解决决策的巨大和多维信息的问题。所提出的模型已被一组实验验证。结果表明，我们的模型为快速数据挖掘和搜索最佳解决方案提供了合理的性能和高适应性。

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