Stock Price Trend Prediction Based on RBF Neural Network and Artificial Fish Swarm Algorithm

被引：0

作者：

Wei Yanming ^{[1
]}

Gan Xusheng ^{[2
]}

Lei Lei ^{[3
]}

机构：

[1] XiJing Coll, Xian 710123, Shaanxi, Peoples R China

[2] Air Force Engn Univ, Air Traff Control & Nav Coll, Xian 710051, Shaanxi, Peoples R China

[3] Henan Univ Econ & Law, Sch Business Adm, Zhengzhou 050046, Henan, Peoples R China

来源：

2017 INTERNATIONAL CONFERENCE ON MATERIALS, ENERGY, CIVIL ENGINEERING AND COMPUTER (MATECC 2017) | 2017年

关键词：

RBF Neural Network; Artificial Fish Swarm Algorithm; Stock Price Trend; Prediction;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Stock market is a complex nonlinear dynamic system, the traditional stock price prediction method is difficult to reveal its inherent law, and the prediction error is larger. Based on this, a prediction method based on artificial Fish Swarm Algorithm (FSA) and RBF Neural Network (RBFNN) is proposed to predict the stock price trend. In method, firstly, a dynamic adjustment method to the algorithm parameters: visual field and movement step is introduced to improve the search capability of AFS, and then modified FSA is used to train the RBFNN model. The simulation shows that, the proposed method is better than BPNN and RBFNN in prediction accuracy for stock price trend. It provides an effective and feasible method for stock price prediction.

引用

页码：210 / 215

页数：6

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