Paper Info
Title
A Hybrid Swarm Optimization For Neural Network Training With Application In Stock Price Forecasting
Abstract
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.
Year
Venue
Field
2016
2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Particle swarm optimization,Swarm behaviour,Computer science,Multi-swarm optimization,Stock price forecasting,Hang,Artificial intelligence,Hybrid swarm,Artificial neural network,Machine learning,Metaheuristic
DocType
ISSN
Citations 
Conference
1062-922X
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Jianjia Pan1185.01
Yuan Yan Tang22662209.20
Yulong Wang38112.26
Xianwei Zheng483.83
Huiwu Luo5377.98
Hao-Liang Yuan600.68
patrick s p wang730347.66