A fast genetic method for inducting linguistically understandable fuzzy models

L. Sánchez
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引用次数: 6

Abstract

Fuzzy rule bases can be regarded as mixtures of experts, and boosting techniques can be applied to learn them from data. In particular, provided that adequate reasoning methods are used, fuzzy models are extended additive models, thus backfitting can be applied to them. We propose to use an implementation of backfitting that uses a genetic algorithm for fitting submodels to residuals and we also show that it is both more accurate and faster than other fuzzy rule learning methods.
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引入语言可理解模糊模型的快速遗传方法
模糊规则库可以看作是专家的混合物,可以使用增强技术从数据中学习它们。特别是,如果使用适当的推理方法,模糊模型是扩展的加性模型,因此可以对其进行反拟合。我们建议使用一种反向拟合的实现,该实现使用遗传算法将子模型拟合到残差上,并且我们还表明它比其他模糊规则学习方法更准确和更快。
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