Multi sensor data fusion method based on fuzzy neural network

  • Ling Youzhu*
  • , Xu Xiaoguang
  • , Shen Lina
  • , Liu Jingmeng
  • *Corresponding author for this work

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

Abstract

With the uncertainty of the multi sensor data of the fuzzy neural network fusion, the measure data from sensors is used to as the input of the fuzzy neural network and then is fuzzed. Next the data is analyzed and disposed by the neural network rule. Finally it is output after defuzzification. Confronting with the input fuzzification with uncertain membership function, we adopt the golden partition method to decide the initial center and width of membership functions of the fuzzification layer. The way of the model fuzzification and the improved BP network study rule is introduced to the network judging rule, and the judging result is output after defuzzification according to the weight rule .The article gives a general method of the multi sensor data gaining based on fuzzy neural network. The structure of network is rational and has rather quick training speed. It also has good generalization ability.

Original languageEnglish
Title of host publicationProceedings - IEEE INDIN 2008
Subtitle of host publication6th IEEE International Conference on Industrial Informatics
Pages153-158
Number of pages6
DOIs
StatePublished - 2008
EventIEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics - Daejeon, Korea, Republic of
Duration: 13 Jul 200816 Jul 2008

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
ISSN (Print)1935-4576

Conference

ConferenceIEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics
Country/TerritoryKorea, Republic of
CityDaejeon
Period13/07/0816/07/08

Keywords

  • Data fusion
  • Fuzzy
  • Neural network
  • Rule

Fingerprint

Dive into the research topics of 'Multi sensor data fusion method based on fuzzy neural network'. Together they form a unique fingerprint.

Cite this