基于模糊神经网络的数据挖掘算法

Wu Jianhui, Su-min Yu, Shao Hongbo, Yin Su-feng, Xue Ling, Hu Bo, Wang Guoli
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引用次数: 1

摘要

本文选择模糊神经网络作为数据挖掘(DM)的算法,将人工神经网络作为一种计算工具引入到模糊逻辑中,利用人工神经网络作为模糊系统的隶属函数、模糊规则和可拓原理,形成一种网络化的描述形式。选择模糊神经网络(FNN)作为数据挖掘算法。将模糊理论与神经网络相结合,利用FNN强大的非线性处理能力,在产生模糊划分神经网络训练后,利用阈值和提取规则进行分类,最后验证了该算法的有效性,与其他模糊神经网络相比,该神经网络具有更快的学习速度和更小的体积。具有良好的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Mining Algorithm Based on Fuzzy Neural Network
In this paper, the fuzzy neural network is selected as the algorithm for data mining (DM), introducing the artificial neural network into the fuzzy logic by treating it as a computing tool, it is a networklized description form by using the artificial neural network as the membership function in a fuzzy system, fuzzy rules and extension principle. The fuzzy neural network (FNN) is selected as the algorithm for data mining. By combining the fuzzy theory with neural network, using the strong nonlinear processing ability of FNN, finding the classification after producing a fuzzy partition neural network training, using thresholds and extracting rules, finally, the validity of this algorithm is verified, comparing with other fuzzy neural network, the neural network is faster learning speed and smaller in size. It owns has a good application prospect.
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