An online rfid localization in the manufacturing shopfloor

Andri Ashfahani*, Mahardhika Pratama, Edwin Lughofer, Qing Cai, Huang Sheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

15 Citations (Scopus)


Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the nonstationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode, where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms.

Original languageEnglish
Title of host publicationPredictive Maintenance in Dynamic Systems
Subtitle of host publicationAdvanced Methods, Decision Support Tools and Real-World Applications
PublisherSpringer International Publishing
Number of pages23
ISBN (Electronic)9783030056452
ISBN (Print)9783030056445
Publication statusPublished - 1 Jan 2019
Externally publishedYes


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