Recurrent neural network training method

Abstract

The invention relates to a recurrent neural network training method, and the method comprises the steps: carrying out the training and prediction of the daily frequency pressure of a database index under a certain system based on an LSTM model, so as to obtain the data trend of the index in a period of time in the future, and obtaining the pressure condition of the database under the system through the calculation of the amplification, and enabling the pressure condition to be used for judging the risk of the database and carrying out the early warning management. A daily frequency pressure index of the database is a processing index, and data preprocessing of missing value complementation is carried out on the data; constructing a complete time series data set (Xt, Tt) n; and after feature combination is completed, starting to train the model. According to the recurrent neural network training method, after data processing and arrangement are carried out on database indexes supported in a computer software system, modeling is carried out in a neural network type machine learning mode, so that the prediction condition of the database indexes in future time is obtained; based on a prediction result, index management can be carried out on a database under the system, and meanwhile, high early warning and control capabilities are achieved under various risks.

Description

technical field [0001] The invention relates to a dynamic process monitoring and generating method for automatic data modeling, and specifically relates to a training method for a cyclic neural network. Background technique [0002] The input and output of traditional neural networks are independent of each other, that is, there is no correlation between the input set and the output set. In real cases, there are indeed many cases where the input set and the output set are related in time or space. [0003] Through a certain method, the input and output sets of each training can be connected to a certain extent during the training of the neural network. Based on this, the recurrent neural network (RNN) creatively introduces the concept of "memory", and by adding memory items, the training process of the neural network can provide a relationship bridge for the input and output sets to a certain extent. [0004] In practice, first try to apply the recurrent neural network mode...

AI Technical Summary

Problems solved by technology

[0005] However, the chain calculation method in actual operation may have a gradient explosion problem, that is, the memory items are superimposed in the cyclic neural network, resulting in the inability to converge the training results in the end.

[0006] Existing recurrent neural network training methods cannot accurately predict data content that changes over time

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