一种基于学习型人工免疫算法的股价预测模型

摘要

针对传统的人工免疫算法中的克隆变异无差异化和BP神经网络容易陷入局部极小的缺点,提出一种结合学习型人工免疫算法与BP算法的新股价预测模型,利用该模型对股票进行股价预测和投资策略分析.该模型克服了人工免疫算法中抗体克隆和抗体变异无差异化的缺陷,并在模型中加入抗体学习功能,提高抗体优化的收敛速度和精度.仿真结果表明,学习型人工免疫算法的股价预测(Stock Price Prediction—Learning Artificial Immune System,SPP-LAIS)模型在投资策略上成功率要优于BP股价预测模型.%In traditional artificial immune algorithm,there is no differentiation in clone step and variation step,and BP neural network is prone to obtain local minimum value.This paper presents a hybrid model combining a learning artificial immune algorithm and BP algorithm for stock shares forecast and investment strategy analysis.This model overcomes the shortcomings of artificial immune algorithm in cloning antibody and antibody variation without differentiation,and adds the antibody learning function in the model,accelerating the convergence speed and accuracy of antibody optimization.The simulation results show that the stock price prediction model with learning artificial immune algorithm is superior to BP stock price prediction model in the stock price prediction accuracy and investment strategy.