A New Approach Of Fuzzy Neural Networks

机译：模糊神经网络的一种新方法

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In this paper, we develop a new approach to obtain fuzzy rule bases and present an improved fuzzy neural networks (FNN) model which consists of four layer neurons. The fuzzy rule bases could be extracted adaptively and directly from training sampled data, and the weights of fuzzy rules reflects the supported grades of the linguistic variables' value of each input to the antecedent fuzzy rules and the important degree of the fuzzy rule in fuzzy rule bases. In the proposed model, the structure and all the parameters such as the membership functions, the weights and so the number of fuzzy bases etc. would be adjusted optimally through back-propagation learning algorithm.

机译：在本文中，我们开发了一种获取模糊规则库的新方法，并提出了一种由四层神经元组成的改进的模糊神经网络（FNN）模型。可以从训练样本数据中自适应地直接提取模糊规则库，模糊规则的权重反映了每个输入对先前模糊规则所支持的语言变量值的支持等级以及模糊规则在模糊规则中的重要程度基地。在该模型中，通过反向传播学习算法可以对结构和所有参数（如隶属函数，权重，模糊基数等）进行最佳调整。

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来源
《World Multiconference on Systemics, Cybernetics and Informatics(SCI 2002) v.6: Industrial Systems and Engineering I; 20020714-20020718; Orlando,FL; US》|2002年|P.467-470|共4页
会议地点 Orlando FL(US);Orlando FL(US)
作者
Xieping Gao; Qi Xu; Jiong Wang;
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作者单位

Xiangtan University Hunan, 411105, PRC;

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会议组织
原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
关键词
fuzzy set; neural networks; fuzzy rule; BP learning algorithm;

机译：模糊集;神经网络;模糊规则; BP学习算法;

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A New Approach Of Fuzzy Neural Networks

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