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首页> 外文期刊>International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems >THREE-PARAMETER FUZZY ARITHMETIC APPROXIMATION OF L-R FUZZY NUMBERS FOR FUZZY NEURAL NETWORKS
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THREE-PARAMETER FUZZY ARITHMETIC APPROXIMATION OF L-R FUZZY NUMBERS FOR FUZZY NEURAL NETWORKS

机译:模糊神经网络的L-R模糊数的三参数模糊算术逼近

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摘要

In this study, we proposed an alternative operation of fuzzy arithmetic on L-R fuzzy numbers by three parameters of mode, left spread and right spread. Then, based on this approximation method, a new learning algorithm of a fully fuzzified neural network was developed in which the L-R fuzzy numbers were considered as the fuzzy signals. While the forward operations of fuzzy signals were based on the proposed three-parameter fuzzy arithmetic approximation method, the backward learning adopted a back-propagation learning procedure with a measurable error function. The learning algorithm was illustrated by an example of the recognition of fuzzy IF-THEN rules. The simulation result showed that the proposed approximation method used in such learning model was efficient and accurate.
机译:在这项研究中,我们提出了由左扩散和右扩散三个参数对L-R模糊数进行模糊算术的替代运算。然后,基于这种近似方法,开发了一种将L-R模糊数作为模糊信号的全模糊神经网络的学习算法。当模糊信号的正向运算基于所提出的三参数模糊算术逼近方法时,反向学习采用具有可测量误差函数的反向传播学习程序。通过识别IF-THEN规则的例子说明了该学习算法。仿真结果表明,该学习模型中使用的近似方法是有效和准确的。

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