A local linear radial basis function neural network for financial time-series forecasting

被引：25
|
作者：
Nekoukar, Vahab [1 ]
Beheshti, Mohammad Taghi Hamidi [2 ]
机构：
[1] Iran Univ Sci & Technol, Dept Elect Engn, Biomed Engn Grp, Tehran, Iran
[2] Tarbiat Modares Univ, Dept Elect Engn, Control Grp, Tehran, Iran
来源：
APPLIED INTELLIGENCE | 2010年 / 33卷 / 03期
关键词：
Local linear radial basis function neural network; Particle swarm optimization with hunter particles algorithm; Time-series prediction; Financial forecasting; PREDICTION;
D O I：
10.1007/s10489-009-0171-1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper a Local Linear Radial Basis Function Neural Network (LLRBFN) is presented. The difference between the proposed neural network and the conventional Radial Basis Function Neural Network (RBFN) is connection weights between the hidden layer and the output layer which are replaced by a local linear model in the LLRBFN. A modified Particle Swarm Optimization (PSO) with hunter particles is introduced for training the LLRBFN. The proposed methods have been applied for prediction of financial time-series and the result shows the feasibility and effectiveness.
引用
收藏
页码：352 / 356
页数：5
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