A rough set based fuzzy neural network algorithm for weather prediction

Kan Li*, Yu Shu Liu

*Corresponding author for this work

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

14 Citations (Scopus)

Abstract

A rough set based fuzzy neural network algorithm is proposed to solve weather prediction. In order to avoid arriving at local minimum value, the least square algorithm (LSA) is used in the learning process of fuzzy neural network to obtain global convergence. Because structure of fuzzy neural networks, the numbers of rules and the initial weights are difficult to be determined, here the rough sets method is introduced to decide the numbers of rules and original weights. Finally, the proposed algorithm through standard data set is testified to have better rationality and availability than BP algorithm. Experiment results show the algorithm for weather prediction may get better effect.

Original languageEnglish
Title of host publication2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages1888-1892
Number of pages5
Publication statusPublished - 2005
EventInternational Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China
Duration: 18 Aug 200521 Aug 2005

Publication series

Name2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Conference

ConferenceInternational Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/TerritoryChina
CityGuangzhou
Period18/08/0521/08/05

Keywords

  • Fuzzy neural network
  • Rough sets
  • Weather prediction

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