基于优化GA-BP神经网络的金融产品营销预测

传统BP神经网络存在着网络结构参数确定过于依赖经验、易于陷入局部解等缺陷，为了改进BP神经网络模型的应用缺陷，提出优化GA-BP算法，通过GA算法优化BP神经网络拓扑结构和网络参数初始值的选取过程，并且为了验证模型的可行性，以某银行短期理财产品营销的客户历史数据作为实证研究对象，并通过与BP神经网络模型的对比实验，验证该模型可以更精确地预测银行理财产品的客户营销结果。实验结果表明将该模型用于对金融产品营销数据的仿真计算，可以更精确地预测未来营销结果。

Financial production marketing prediction based on optimization GA-BP neural network

The traditional BP neural network has some application problems. For example, the network structure parameter is too dependent on experience and easy to fall into local solution. In order to improve the application defects of BP neural network model, the optimization GA-BP algorithm to optimize BP neural network topology and the selection process of network initial parameter value is proposed. In order to verify the feasibility of the model, marketing customer historical data of a bank short-term financial products as the research object is used to validate the model which could more accurately predict the customer compared with BP neural network model. The test results show that the model could be applied to analysis financial product marketing data and more accurately predict the future marketing results.