Fuzzy support vector machines for device-free localization

Yi Yuan Chiang, Wang Hsin Hsu, Sheng Cheng Yeh, Yi Chen Li, Jung Shyr Wu

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

9 Scopus citations

Abstract

In this paper, we develop a novel fuzzy support vector machine for device-free localization. The fuzzy support vector machine is an integration of support vector machines (SVMs) and fuzzy systems; therefore a fuzzy system can be extracted from an SVM. We not only show how to integrate SVMs and fuzzy systems, but also show how to reduce the complexity of the obtained fuzzy systems. One major benefit of reducing the complexity of fuzzy systems is that the obtained fuzzy systems are easy to be optimized. The proposed method is proved to be effective through experimental studies, which is carried in a badminton court in which four WiFi access points and 17 test points are deployed. The simulation results show the reduced fuzzy system is easy to perform optimization and generates better results than pure SVM. An simulation result shows the correctness of pure SVM is 66.8% and the correctness of optimized fuzzy systems is 74.6%.

Original languageEnglish
Title of host publication2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings
Pages2169-2172
Number of pages4
DOIs
StatePublished - 2012
Event2012 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2012 - Graz, Austria
Duration: 13 May 201216 May 2012

Publication series

Name2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings

Conference

Conference2012 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2012
Country/TerritoryAustria
CityGraz
Period13/05/1216/05/12

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