Improved Elman neural network prediction method based on noise reduction algorithm

A technology of neural network and prediction method, which is applied in the field of computer neural network application, can solve the problems of high resource consumption and single-dimensional time series data prediction, and achieve the effects of low time and resources, strong long-term dependence on time, and excellent memory ability

Pending Publication Date: 2021-06-11
SOUTH CHINA UNIV OF TECH
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 imp

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Improved Elman neural network prediction method based on noise reduction algorithm
  • Improved Elman neural network prediction method based on noise reduction algorithm
  • Improved Elman neural network prediction method based on noise reduction algorithm

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0061] Embodiment:

[0062] This embodiment provides a prediction method of improving ELMAN neural network based on noise reduction algorithm, as shown, including the following steps:

[0063] S1, obtain the physical machine resource usage time series as the original data, divide the original data into the training set, test set, and verification set, and focused on the training set, test set and verification of the training set, test set and verification in accordance with a given time window and predicted steps The data is sampled.

[0064] Such as figure 2 As shown, the training data is organized according to the form of training, and the original time series data is divided into training sets, verification sets and test sets. When dividing, the proportion of 8: 1: 1 is generally divided into training set, verification set and test set. At the time of segmentation training set, 80% of the data from the starting point of the original data, 10% of the remaining data is the data o...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62G06N3/08G06N3/04
CPCG06N3/08G06N3/045G06F2218/04G06F2218/12G06F18/25
Inventor 刘发贵张永德
Owner SOUTH CHINA UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap