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Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization

机译:基于粒子群算法的模糊认知图学习

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This paper introduces a new learning algorithm for Fuzzy Cognitive Maps, which is based on the application of a swarm intelligence algorithm, namely Particle Swarm Optimization. The proposed approach is applied to detect weight matrices that lead the Fuzzy Cognitive Map to desired steady states, thereby refining the initial weight approximation provided by the experts. This is performed through the minimization of a properly defined objective function. This novel method overcomes some deficiencies of other learning algorithms and, thus, improves the efficiency and robustness of Fuzzy Cognitive Maps. The operation of the new method is illustrated on an industrial process control problem, and the obtained simulation results support the claim that it is robust and efficient.
机译:本文介绍了一种新的模糊认知图学习算法,该算法基于群体智能算法的应用,即粒子群算法。所提出的方法用于检测将模糊认知图引导至所需稳态的权重矩阵,从而完善专家提供的初始权重近似值。这是通过最小化适当定义的目标函数来完成的。这种新颖的方法克服了其他学习算法的一些不足,从而提高了模糊认知图的效率和鲁棒性。在工业过程控制问题上说明了该新方法的操作,并且所获得的仿真结果支持了该方法的鲁棒性和有效性。

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