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Genetic-algorithm-based type reduction algorithm for interval type-2 fuzzy logic controllers

机译：区间2型模糊逻辑控制器的基于遗传算法的类型约简算法

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摘要

In interval type-2 fuzzy logic controllers (IT2-FLCs), the output processing includes type reduction and defuzzification. Recently, researchers have proposed many efficient type reduction algorithms, but there are no effective schemes to improve the output of defuzzification. This paper presents a genetic-algorithm-based type reduction algorithm, which reduces the type of an interval type-2 fuzzy set and provides optimal defuzzified output from the type-reduced set In addition, the proposed type reduction is executed offline (in other words, the controller has been reduced to type-1 in practical applications), which significantly reduces the computational cost and facilitates the design of controllers that operate in real time. To demonstrate the effectiveness of the proposed method, truck backing control problems are utilized. The results show that the proposed method outperforms general IT2-FLCs in terms of speed, computational cost, and robustness.
机译：在区间类型2模糊逻辑控制器（IT2-FLC）中，输出处理包括类型归约和去模糊化。最近，研究人员提出了许多有效的类型减少算法，但是还没有有效的方案来提高去模糊化的输出。本文提出了一种基于遗传算法的类型约简算法，该算法可减少区间类型2模糊集的类型，并从类型约简集提供最佳的去模糊输出。此外，建议的类型约简可离线执行（换句话说，就是，在实际应用中将控制器减少为Type-1），从而显着降低了计算成本，并简化了实时操作控制器的设计。为了证明所提出方法的有效性，利用了卡车后备控制问题。结果表明，该方法在速度，计算成本和鲁棒性方面均优于一般的IT2-FLC。

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