  {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 non-stationary 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.
