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Optimizing fuzzy membership function using dynamic multi swarm — PSO

机译:使用动态多群算法优化模糊隶属度函数

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Performance of fuzzy application to solve the control problems depends on a number of parameters such as the choice and shape of the membership function. Defining MFs manually in a proper way is time consuming, prone to errors and difficult. And especially it depends subjectively based on expert's experiences. Improvement of the performance of the fuzzy control system is made by the optimization of the membership function. In this paper, a Dynamic Multi-Swarm PSO is used to optimize the fuzzy membership function. DMS-PSO has the ability to avoid local optimal and able to generate an optimal set of parameters for fuzzy control system. The experiment carried out with real-life application, park a vehicle into garage beginning from any starting position; results show that the better performance of proposed fuzzy model is obtained by using the optimized membership functions than a simple fuzzy model when the membership functions were heuristically defined.
机译:模糊应用程序解决控制问题的性能取决于许多参数,例如隶属函数的选择和形状。以正确的方式手动定义MF非常耗时,容易出错且困难。特别是,它主观地取决于专家的经验。通过隶属函数的优化来改善模糊控制系统的性能。本文采用动态多群粒子群优化算法对模糊隶属度函数进行优化。 DMS-PSO能够避免局部最优,并能够为模糊控制系统生成最优的一组参数。实验是通过实际应用进行的,将车辆从任何起始位置停放在车库中;结果表明,当启发式定义隶属度函数时,使用优化的隶属度函数比简单的模糊模型具有更好的性能。

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