摘要
针对传统的RBF神经网络在选取中心矢量参数时的不足,提出用具有较强跳出局部最优的布谷鸟算法(CS)优化RBF神经网络的中心矢量的改进算法,并将该算法应用于股票价格的预测,仿真结果表明:该算法的预测精度比传统的RBF算法的预测精度高,是一种有效的股票预测方法。
Aimed at the disadvantage of selecting center vectors parameters of RBF neural network, an improved algorithm is proposed to optimize RBF neural network＆#39;s center vectors, based on Cuckoo Search algorithm with strong jumping out of local optimum, and it is applied to predict the stock price. Simulation result shows the new algorithm＆#39;s prediction accuracy is higher than the tradition RBF＆#39;s, and it is an effective method of stock forecasting.
出处
《价值工程》
2014年第31期142-143,共2页
Value Engineering
基金
陕西省教育厅自然科学专项基金(12JK0862)
西安工程大学研究生创新基金项目(chx2013022)
关键词
RBF神经网络
布谷鸟算法
主成分分析
股票价格预测
RBF neural network
Cuckoo Search algorithm
Principal Component Analysis
stock price prediction
