Research on investor age prediction based on the PSO-BP algorithm
The standard BP neural network algorithm lacks specific and clear theoretical guidance during the training process,which can easily lead to problems such as local minima and the contradiction between generalization ability and training.There-fore,a particle swarm optimization(PSO)algorithm is proposed to improve the BP neural network model,selecting five input vari-ables:asset,income,debt,economic demand,and time to predict the age of ordinary investors,then made a comparative analysis with the standard BP algorithm,The experimental results show that the PSO-BP algorithm has lower mean square error(MSE),mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)in evaluation indicators compared to the standard BP algorithm.The PSO-BP algorithm can more accurately predict the age value of ordinary investors with smaller error values,which has certain reference value for investment orientation research of investors.
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