An Online RFID Localization in the Manufacturing Shopfloor

RFID technology has gained popularity for cheap and reliable localization applications. In the realm of manufacturing shopfloor, it can be used for tracking the location of moving manufacturing objects to achieve greater efficiency. The underlying challenge of localization in the manufacturing shopfloor lies in the nonstationary characteristics of actual environments which calls for an adaptive lifelong 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 capable of delivering comparable accuracy compared to state-of-the-art algorithms.