Stock Price Prediction of the BP Neural Network Optimzed by the Grey Wolf Optimizer
The method of improving the BP neural network by the grey wolf optimizer is discussed,in order to im-prove the training effect and performance of the BP neural network.Firstly,the basic principle of the BP neural net-work and the basic concept of the grey Wolf optimizer are introduced.Then,the grey wolf optimizer is applied to the process of optimizing the weight and bias value of the BP neural network,and the error function is reduced by adjusting these parameters,so as to improve the accuracy and convergence speed of the network.Experimental re-sults show that the BP neural network optimized by the grey wolf optimizer has good performance and generaliza-tion ability.Next,empirical analysis is carried out with stock data,showing that the model has high accuracy and stability in stock price prediction and can provide effective reference for investors to make decisions.Finally,the contribution of this study and the future research direction are summarized.
Gray wolf optimizerBP neural networkParameter optimizationStock price prediction
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万方数据
