股票价格短期预测的LM遗传神经网络算法

Stock price short-time prediction based on GA-LM-BP neural network

针对传统神经网络在股票价格预测中易陷入局部最优和预测精度偏低的问题,提出了一种改进的神经网络算法进行求解。改进的算法基于LM算法建立了改进的三层BP神经网络对股票价格建立预测模型,避免算法陷入局部最优,并运用遗传算法优化神经网络的权值和阈值,提高神经网络对股票价格的预测精度。采用Matlab对上述算法模型进行仿真测试,通过预测4支股票价格的实验证明改进后的神经网络预测算法对股票价格的短期预测有较好的精度和稳定性。

In order to deal with the problems of local optima and low predicting accuracy m stock price preOlcnon using traditional neural networks, Levenberg-Marquardt （LM） algorithm is introduced to improve the three-layer back propagation （BP） neural networks in this paper to prevent the local optima. Genetic algorithm （GA） is used to optimize the weight values and thresholds in BP nettral networks to obtain high accuracy of predicting the stock price. The proposed method is simulated in Matlab and the experimental results of predicting price of 4 stocks show the proposed algorithm is effective and promising.