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Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method

机译：遗传模糊逻辑控制器：一种具有新编码方法的迭代进化算法

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摘要

Logic rules and membership functions are two key components of a fuzzy logic controller (FLC). If only one component is learned, the other one is often set subjectively thus can reduce the applicability of FLC. If both components are learned simultaneously, a very long chromosome is often needed thus may deteriorate the learning performance. To avoid these shortcomings, this paper employs genetic algorithms to learn both logic rules and membership functions sequentially. We propose a bi-level iterative evolution algorithm in selecting the logic rules and tuning the membership functions for a genetic fuzzy logic controller (GFLC). The upper level is to solve the composition of logic rules using the membership functions tuned by the lower level. The lower level is to determine the shape of membership functions using the logic rules learned from the upper level. We also propose a new encoding method for tuning the membership functions to overcome the problem of too many constraints. Our proposed GFLC model is compared with other similar GFLC, artificial neural network and fuzzy neural network models, which are trained and validated by the same examples with theoretical and field-observed car-following behaviors. The results reveal that our proposed GFLC has outperformed.
机译：逻辑规则和隶属函数是模糊逻辑控制器（FLC）的两个关键组成部分。如果仅学习一个组件，则通常主观地设置另一个组件，因此会降低FLC的适用性。如果同时学习两个组件，则通常需要很长的染色体，因此可能会降低学习性能。为了避免这些缺点，本文采用遗传算法顺序学习逻辑规则和隶属函数。我们在选择逻辑规则和调整遗传模糊逻辑控制器（GFLC）的隶属函数时提出了一种双层迭代演化算法。上层是使用下层调整的隶属函数来解决逻辑规则的组成。下层是使用从上层学习的逻辑规则来确定隶属函数的形状。我们还提出了一种用于调整隶属度函数的新编码方法，以克服约束过多的问题。我们提出的GFLC模型与其他类似的GFLC模型，人工神经网络和模糊神经网络模型进行了比较，这些模型通过相同的示例进行了训练和验证，并具有理论和现场观察的跟车行为。结果表明，我们提出的GFLC的表现要好。

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