Improved Elman neural network prediction method based on noise reduction algorithm

Abstract

The invention discloses an improved Elman neural network prediction method based on a noise reduction algorithm. The prediction method comprises the following steps: S1, dividing original data into a training set, a test set and a verification set in proportion; S2, performing noise reduction on the original data by adopting a noise reduction algorithm CPW, decomposing data of different dimensions in the original data by adopting CEEMDAN through the noise reduction algorithm CPW, performing noise reduction on the decomposed IMF by combining permutation entropy and using wavelet transform, and then reconstructing a sequence subjected to noise reduction processing into a time sequence subjected to noise reduction; S3, constructing an EAMC neural network combined with an attention mechanism; S4, putting the data of the training set after noise reduction into a neural network for training, storing the trained neural network after a loss value is smaller than a given threshold value, and ending the training; and S5, inputting the to-be-tested sample into the neural network obtained after training to obtain a prediction result, thereby improving the prediction precision.

Description

technical field [0001] The invention belongs to the technical field of computer neural network application, and in particular relates to a prediction method of an improved Elman neural network based on a noise reduction algorithm. Background technique [0002] In recent years, under the background of the country's vigorous call for the "Internet +" development model and the gradual commercialization of 5G, more and more storage and services are deployed on the cloud. This makes the load on the cloud data center more and more heavy. To complete huge computing and storage services in the data center, it needs to consume a lot of power resources. Traditional task scheduling algorithms have a weak perception of energy consumption. If the resource usage of physical machines in the future can be obtained in advance, the scheduling algorithm can make scheduling decisions in advance based on the predicted situation. Therefore, the effect of task scheduling is improved and the tim...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a time series prediction method based on improved ENN and noise reduction algorithm, in order to improve the accuracy of network prediction while solving the problems of single-dimensional and resource-consuming time series data prediction in existing methods

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