模糊系统建模与近似推理的核方法

Yongyi Chen, Hanzhong Feng
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引用次数: 1

摘要

模糊系统建模是近二十年来一个活跃的研究课题。一般来说,文献中提出了两种方法:1)将专家的先验知识转化为模糊规则;2)从给定的训练数据中学习一组模糊规则。第一种方法适用于容易获得先验知识和训练数据不充分的情况。然而,在许多应用中,训练数据的量通常很大。在这种情况下,第二种方法可能比第一种方法提供更客观的规则。本文证明了一类模糊系统本质上是核机。因此,支持向量机(SVM)方法可以用于构建模糊系统。该方法已在实际天气预报数据上进行了验证。实验结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A kernel method for fuzzy systems modeling and approximate reasoning
Fuzzy systems modeling has been an active research topic for almost twenty years. In general, two approaches have been proposed in the literature: 1) translate experts' prior knowledge into fuzzy rules; 2) learn a set of fuzzy rules from given training data. The first approach applies to the case that prior knowledge can be easily obtained and training data are not sufficient. However, in many applications, the amount of training data is usually large. In that case, the second approach may provide more objective rules than the first approach. In this paper, we show that a class of fuzzy systems is in essence kernel machines. Therefore, the support vector machine (SVM) method can be used to construct fuzzy systems. This method has been tested on real weather forecast data. Experimental results demonstrate the effectiveness of the method.
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