A generic fuzzy aggregation operator: rules extraction from and insertion into artificial neural networks

被引：0
|
作者：
C. J. Mantas
机构：
[1] University of Granada,Department of Computer Science and Artificial Intelligence
来源：
Soft Computing | 2008年 / 12卷
关键词：
Fuzzy aggregation; Artificial neural networks; Additive fuzzy systems;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Multilayered feedforward artificial neural networks (ANNs) are black boxes. Several methods have been published to extract a fuzzy system from a network, where the input–output mapping of the fuzzy system is equivalent to the mapping of the ANN. These methods are generalized by means of a new fuzzy aggregation operator. It is defined by using the activation function of a network. This fact lets to choose among several standard aggregation operators. A method to extract fuzzy rules from ANNs is presented by using this new operator. The insertion of fuzzy knowledge with linguistic hedges into an ANN is also defined thanks to this operator.
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页码：493 / 514
页数：21
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