Earticle

초록

영어

Data fusion in wireless sensor network is a effective method to reduce the network energy consumption. In order to build high performance of data fusion system, a data fusion algorithm using BP neural network to optimize fuzzy prediction and train the membership degree of collecting data is presented, which is used to determine which kind of dividing fusion mechanism is belonged for the sensor’s data collected at a given moment. First the fuzzy prediction is used for acquisition of knowledge which data is simplified to remove redundant properties and samples. The BP neural network is used to process fuzzy prediction and finally the patterns of muli-sensed-data(temperature as an example) fusion distribution are formed. Two kinds of different BP neural network are proposed and compared for more precision of the fuzzy prediction result. Second data fusion based on the fuzzy prediction will be implemented to reduce the number of data transmission in the network. Simulation results show that the algorithm has good precision and applicability.

목차

Root
 Abstract
 1. Introduction
 2. Related Works
 3. Data Fusion Algorithm
  3.1 Network Structure’s Establishment
  3.2 The Network Learning Algorithm
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Tian Le [ Institute of Electricity and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China, 100044 ]
• Zhao Jing [ Institute of Electricity and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China, 100044 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605