Data fusion using fuzzy measures and genetic algorithms

Author

Tong, Shuhong ; Shen, Yi ; Liu, Zhiyan

Author_Institution

Dept. of Control Eng., Harbin Inst. of Technol., China

Volume

2

fYear

2000

fDate

2000

Firstpage

1113

Abstract

This paper proposes an improvement on the fusion method presented previously (1994, 1998). In those methods not only the reliabilities of the sensors are not considered but also the choice of parameter k is relevant to the number of sensors and whether there is opinion close to 0.5. In our method Genetic Algorithms (GA) is used to find the optimal values for the reliabilities of sensors and fuzzy inference rules for determining the parameter k in multi-sensor fusion. Multi-step fusion and one-step fusion methods are formed based on the fusion functions. Simulation results show the effectiveness of the proposed methods

Keywords

fuzzy systems; genetic algorithms; inference mechanisms; sensor fusion; fuzzy inference rules; genetic algorithms; multi-sensor fusion; multi-step fusion; one-step fusion; optimal values; parameter k; reliabilities; sensors; Control engineering; Data mining; Genetic algorithms; Optimized production technology; Robustness; Sensor fusion; Sensor systems; Set theory; Telephony; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 2000. IMTC 2000. Proceedings of the 17th IEEE

Conference_Location

Baltimore, MD

ISSN

1091-5281

Print_ISBN

0-7803-5890-2

Type

conf

DOI

10.1109/IMTC.2000.848930

Filename

848930