Research and Example of Hidden Layer Structure Based on BP Neural Net-work
BP neural network is the most commonly used network in neural networks.With the development of artificial intelligence technology,various industries have gradually applied BP neural network in daily life,such as prediction,recommendation,recognition,and other fields,and have achieved certain results.But as the amount of data increases,the BP neural network also faces problems such as gradient descent when making predictions,and experts are constantly ad-justing the algorithm and network structure.The design of the hidden layer structure in BP net-works has always been uncertain,especially the determination of the number of hidden layer u-nits lacks theoretical basis,and designers mostly rely on experience to determine.The BP net-work is most widely used in neural networks,one of which is its application in function conver-gence.This article mainly analyzes the convergence of functions on BP networks by studying the problem of determining the number of hidden layers and units.By comparing the convergence under different hidden layer layers and hidden layer nodes,the influence of hidden layer struc-ture on function convergence is studied,and the analysis results are applied to stock price predic-tion.Practice has shown that determining the number of hidden layer nodes can improve predic-tion errors to a certain extent.
BP neural networkhidden layerstructure function of convergenceprediction