基于模糊教学输入的神经专家系统

Y. Hayashi
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引用次数: 26

摘要

作者先前(1990,1991)提出了一种模糊神经专家系统,并提供了一种从训练好的神经网络中自动提取模糊IF-THEN规则的方法。在此基础上,提出了一种基于模糊教学输入的神经专家系统。神经专家系统可以对具有模糊教学输入的训练数据中得到的信息进行泛化,并以模糊神经网络的形式将知识具体化,其中模糊教学输入由领域专家主观上给出;并从训练好的神经网络中提取具有先行词(IF-part)中每个命题的语言相对重要性的模糊IF-THEN规则。提出了一种从具有模糊教学输入的训练数据生成的训练神经网络中自动提取模糊IF-THEN规则的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A neural expert system using fuzzy teaching input
The author previously (1990, 1991) proposed a fuzzy neural expert system and provided a method to extract automatically fuzzy IF-THEN rules from a trained neural network. The previous work is extended and a neural expert system is proposed using fuzzy teaching input. The neural expert system can perform generalization of the information derived from training data with fuzzy teaching input and embodiment of knowledge in the form of a fuzzy neural network, where the fuzzy teaching input is subjectively given by domain experts: and extraction of fuzzy IF-THEN rules with linguistic relative importance of each proposition in an antecedent (IF-part) from a trained neural network. A method is proposed to extract automatically fuzzy IF-THEN rules from the trained neural network generated by training data with fuzzy teaching input.<>
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