A hybrid swarm optimization for neural network training with application in stock price forecasting

被引：0

作者：

Pan, Jianjia ^{[1
]}

Tang, Yuan Yan ^{[1
]}

Wang, Yulong ^{[1
]}

Zheng, Xianwei ^{[1
]}

Luo, Huiwu ^{[1
]}

Yuan, Haoliang ^{[2
]}

Wang, Patrick Shen Pei ^{[2
]}

机构：

[1] Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau, Macau, Peoples R China

[2] Northeastern Univ, Coll Comp & Informat Sci, Boston, MA 02115 USA

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2016年

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

A improved swarm optimization method based on particle swarm optimization (PSO) and simplified swarm optimization (SSO) is proposed to adjust the weight in artificial neural network. This method is a modification of traditional PSO and SSO, and combines them to a new optimization method (PSOSSO for short). The proposed method overcomes some of the drawbacks of SSO and improves its ability to train the weight of ANN. In the experiments, the PSOSSO is employed to train fuzzy wavelet neural network (FWNN) forecasting model to predict the prices of Hong Kong Hang Seng Index. The experimental results present that the PSOSSO is more efficient than traditional PSO and SSO methods.

引用

页码：4450 / 4453

页数：4

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