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The Prediction of Lead-Acid Battery Remaining Capacity Based on Improved Ant Colony Algorithm and BP Network(PDF)

《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

Issue:
2016年06期
Page:
95-99
Research Field:
计算机与控制工程
Publishing date:

Info

Title:
The Prediction of Lead-Acid Battery Remaining Capacity Based on Improved Ant Colony Algorithm and BP Network
Author(s):
XUE PingSONG Yan-liang
(School of Automation, Harbin University of Science and Technology, Harbin 150080, China)
Keywords:
solar energy lead-acid battery remaining capacity improved ant colony algorithm ant colony with BP network 
PACS:
TK02
DOI:
10.15938/j.jhust.2016.06.018
Abstract:
Artificial neural networks simulate the human brain network by way of memory,processing information with high intelligence.In recent years,it is widely used in lead-acid battery remaining power prediction research,However,convergence is slow,sensitive to initial and easier to fall into local minimum is a single neural network algorithm shortcomings which difficult to resolve.In view of this situation,this project will improve the ant colony algorithm with BP neural network integration.First,training BP neural network weighting parameters globally with improved ant colony algorithm,and then,going to further partial learning with the BP neural network algorithm,so as to get the best BP neural network weights.Finally,has been certified that this method can promote the rate of convergence visibly and prove the accuracy of the prediction of the BP networks,and can predict the remaining capacity of the battery accurately.

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Last Update: 2017-02-28