A MODIFIED NEURAL-NETWORK-BASED GM(1,1)

被引：0

作者：

Yeh, Ming-Feng ^{[1
]}

Chen, Ti-Hung ^{[2
]}

Lu, Hung-Ching ^{[3
]}

机构：

[1] Lunghwa Univ Sci & Technol, Dept Elect Engn, Taoyuan, Taiwan

[2] Lunghwa Univ Sci & Technol, Dept Comp Informat & Network Engn, Taoyuan, Taiwan

[3] Tatung Univ, Dept Elect Engn, Taoyuan, Taiwan

来源：

PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1 | 2018年

关键词：

Forecasting model; Gradient descent method; Grey model; Neural network; MODEL;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Inspired by neural-network-based GM(1,1) (NN-GM(1,1)), this study attempts to drive a more simple and efficient learning rule to enhance the fitting/forecasting ability and convergence speed of the grey neural network. Simulation results on two real datasets show that the proposed modified NN-GM(1,1) performs better than the original NN-GM(1,1) in terms of fitting and forecasting accuracies. In addition, the modified NN-GM(1,1) has faster convergence speed.

引用

页码：1 / 5

页数：5

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