基于改进型粒子群优化算法的BP网络在股票预测中的应用

A BP Neural Network Based on Improved Particle Swarm Optimization and Its Application in Stock Forecasting

本文提出了基于改进型粒子群优化的BP网络学习算法。在该算法中,首先改进了传统的BP算法,有效地使得网络中输入层、隐含层和输出层结点个数达到一个最优解。然后,用粒子群优化算法替代了传统BP算法中的梯度下降法,使得改进后的算法具有不易陷入局部极小、泛化性能好等特点,并将该算法应用在了股票预测的应用设计中。结果证明:该算法能够明显减少迭代次数,提高收敛精度,其泛化性能也优于传统BP算法。

A BP neural network learning algorithm based on improved Particle Swarm Optimization（PSO） is proposed in this paper. In this algorithm, the conventional BP algorithms are improved first;and the crunodes of the input layer, the hidden layer and the output layer in the neural network reach an optimum solution. However, the gradient descent method in traditional BP algorithms is substituted by the PSO algorithm,and the improved algorithm does not easily trap into a local minima and has a better generalization performance. The algorithm is applied to the application design of the stock prediction for the Shanghai Stock indexes. The results show that this algorithm can reduce the number of iterations obviously,and increase the precision of convergence, and its generalization performance is superior to the traditional BP algorithms.