基于启发式算法的模糊逻辑控制器优化

S. Unsal, Ibrahim Aliskan
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引用次数: 2

摘要

模糊控制器的结构中有很多设计参数。在确定这些参数时,通常使用没有系统方法的传统方法。然而,以这种方式设置控制器参数会导致长时间的实验,这需要花费大量的时间。因此,模糊控制器的设计参数通常采用启发式算法确定。因为,启发式算法可以为无法获得精确解的问题提供非常接近最优解的解。本文采用粒子群算法和遗传算法对模糊控制器的输出隶属度函数进行优化。详细说明了设计和优化阶段,并对结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of Fuzzy Logic Controller by Using Heuristic Algorithms
There are many design parameters in the structure of fuzzy logic controllers. Conventional methods that don't have a systematic approach are often used in determining of these parameters. However, setting the controller parameters in this way leads to long experiments and this takes a lot of time. For this reason, design parameters of the fuzzy logic controller are usually determined by using heuristic algorithms. Because, heuristic algorithms can offer solutions that are very close to the optimal solution for the problems where exact solution cannot be obtained. In this study, output membership functions of a fuzzy logic controller are optimized using particle swarm optimization and genetic algorithm. Design and optimization stages are explained in detail and results are compared with each other.
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