多输入模糊推理采用记忆网络方法和全局/局部推理方法

Proceedings of 1995 IEEE International Conference on Fuzzy Systems. Pub Date : 1995-03-20 DOI:10.1109/FUZZY.1995.409738

H. Arikawa, M. Mizumoto

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引用次数: 0

摘要

在传统的模糊推理中，当输入数量增加时，处理速度(即FLIPS)降低，模糊规则与输入标签的组合数量被认为呈指数增长。本文旨在利用记忆网络方法和全局/局部推理方法来改进这两个问题

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Multi-input fuzzy reasoning employing memory network method and global/local reasoning method

In conventional fuzzy reasoning, when the number of input increases, the processing speed (i.e. FLIPS) decreases and the number of fuzzy rules in combination with input labels is thought to increase exponentially. This paper aims to improve these two problems by using the memory network method and global/local reasoning method.<>

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Proceedings of 1995 IEEE International Conference on Fuzzy Systems.

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