A hybrid algorithm of GA wavelet-BP neural networks to predict near space solar radiation

Jianmin Su, Bifeng Song, Baofeng Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Solar radiation is affected by many factors, solar radiation prediction is a highly nonlinear problem. It is hard to establish any analytical mathematical model. Considering solar radiation ray is composed of a series of different frequency bands with different characteristics, wavelet is introduced to decompose the radiation signal into high and low frequency hefts. By respectively inputting the hefts into BP neural networks, which have strong fault-tolerant ability and nonlinear mapping ability, better prediction precision can be obtained. But BP neural networks are apt to converge at local optimal point, so genetic algorithm is embedded to optimize BP neural networks' weights and threshold values,hybrid algorithm's prediction precision is receivable through these improvements.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings
Pages442-450
Number of pages9
EditionPART 2
DOIs
StatePublished - 2009
Event6th International Symposium on Neural Networks, ISNN 2009 - Wuhan, China
Duration: 26 May 200929 May 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5552 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Symposium on Neural Networks, ISNN 2009
Country/TerritoryChina
CityWuhan
Period26/05/0929/05/09

Keywords

  • BP neural networks
  • Genetic algorithm
  • Hybrid algorithm
  • Solar radiation prediction
  • Wavelet analysis

Fingerprint

Dive into the research topics of 'A hybrid algorithm of GA wavelet-BP neural networks to predict near space solar radiation'. Together they form a unique fingerprint.

Cite this