基于组合灰色神经网络的决策管理在电子政务中的应用

Zhiming Qu
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引用次数: 0

摘要

利用灰色系统、满意度挖掘(DM)技术和径向基函数(RBF)神经网络方法,建立了灰色系统与RBF神经网络的组合模型,旨在解决电子政务问题。结果表明,在短期预测中,灰色系统是一种有效的方法,RBF具有较好的学习能力。组合灰色神经网络(CGNN)在结合时变序列满足的情况下,具有趋势和波动的双重性质。结果表明,在电子政务中,CGNN与其他趋势预测方法和简单因子相比有很大的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of DM in E-Government Based on Combined Grey Neural Network
Using grey system, satisfaction mining (DM) technology and radial basis function (RBF) neural network method, the combined model of grey system and RBF neural network is setup, which aims at solving the problems of E-government. The results show that, in short-term prediction, grey system is an effective way and RBF has perfect ability to study. The combined grey neural network (CGNN) has the dual properties of trend and fluctuation under the condition of combining with the time-dependent sequence satisfaction. It is concluded that great improvement comparing with any methods of trend prediction and simple factor in CGNN is stated and described in E-government.
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