A neuro-fuzzy method to learn fuzzy classification rules from data
A neuro-fuzzy method to learn fuzzy classification rules from data
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DOI：
10.1016/s0165-0114(97)00009-2
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发表时间：
1997-08-01
影响因子：
3.9
通讯作者：
Kruse, R
中科院分区：
文献类型：
--
作者：
Nauck, D;Kruse, R
近年来，神经模糊系统在研究和应用方面获得了很大的兴趣。我们所理解的神经模糊模型是使用局部学习策略来学习模糊集和模糊规则的模糊系统。神经模糊技术已经发展到支持发展，如模糊控制器和模糊分类器。本文讨论了一种模糊分类规则的学习方法。该学习算法是一种简单的启发式算法，能够快速地从一组训练数据中导出模糊规则，并通过修改隶属函数的参数来调整模糊规则。我们的方法是基于NEFCLASS，一种用于模式分类的神经模糊模型。我们还讨论了我们在网上免费提供的NEFCLASS软件实现所获得的一些结果。(C) 1997爱思唯尔科学有限公司
Neuro-fuzzy systems have recently gained a lot of interest in research and application. Neuro-fuzzy models as we understand them are fuzzy systems that use local learning strategies to learn fuzzy sets and fuzzy rules. Neuro-fuzzy techniques have been developed to support the development of e.g. fuzzy controllers and fuzzy classifiers. In this paper we discuss a learning method for fuzzy classification rules. The learning algorithm is a simple heuristics that is able to derive fuzzy rules from a set of training data very quickly, and tunes them by modifying parameters of membership functions. Our approach is based on NEFCLASS, a neuro-fuzzy model for pattern classification. We also discuss some results obtained by our software implementation of NEFCLASS, which is freely available on the Internet. (C) 1997 Elsevier Science B.V.