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Research on Optimization of Fuzzy Membership Function Based on Ant Colony Algorithm

机译:基于蚁群算法的模糊隶属度函数优化研究

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摘要

The successful application of fuzzy control depends on some subjectively decided parameters, such as fuzzy membership functions. In this paper, a new method utilizing ant colony algorithm (ACA) was proposed to optimize the fuzzy membership function''s parameters. The subjectivity and blindness were avoided by using this method in the process of designing the input and output membership function. The fuzzy controller optimized by ACA has been applied to control the shipping course and the simulation results show good control performance
机译:模糊控制的成功应用取决于一些主观决定的参数,例如模糊隶属度函数。提出了一种利用蚁群算法(ACA)优化模糊隶属度函数参数的新方法。在设计输入和输出隶属度函数的过程中使用这种方法避免了主观性和盲目性。应用ACA优化的模糊控制器控制了运输过程,仿真结果表明了良好的控制性能。

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