基于PCA的CS-RBF网络的股票预测研究

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'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's prediction accuracy is higher than the tradition RBF's, and it is an effective method of stock forecasting.