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A fast genetic method for inducting descriptive fuzzy models

机译：引入描述性模糊模型的快速遗传方法

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摘要

Under certain inference mechanisms, fuzzy rule bases can be regarded as extended additive models. This relationship can be applied to extend some statistical techniques to learn fuzzy models from data. The interest in this parallelism is twofold: theoretical and practical. First, extended additive models can be estimated by means of the matching pursuit algorithm, which has been related to Support Vector Machines, Boosting and Radial Basis neural networks learning; this connection can be exploited to better understand the learning of fuzzy models. In particular, the technique we propose here can be regarded as the counterpart to boosting fuzzy classifiers in the field of fuzzy modeling. Second, since matching pursuit is very efficient in time, we can expect to obtain faster algorithms to learn fuzzy rules from data. We show that the combination of a genetic algorithm and the backfitting process learns faster than ad hoc methods in certain datasets.
机译：在某些推理机制下，模糊规则库可以看作是扩展的加性模型。这种关系可以用来扩展一些统计技术，以便从数据中学习模糊模型。对这种并行性的兴趣是双重的：理论上和实践上。首先，可以通过匹配追踪算法估计扩展的加性模型，该算法与支持向量机，Boosting和径向基神经网络学习有关；这种联系可以被用来更好地理解模糊模型的学习。特别地，我们在此提出的技术可以看作是模糊建模领域中增强模糊分类器的对应方法。其次，由于匹配追踪在时间上非常有效，因此我们可以期望获得更快的算法来从数据中学习模糊规则。我们表明，在某些数据集中，遗传算法和逆拟合过程的组合学习比临时方法要快。

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