An Online RFID Localization in the Manufacturing Shopfloor

TL;DR

This paper introduces an adaptive, evolving RFID localization system for manufacturing shopfloors using a novel Type-2 Quantum Fuzzy Neural Network, capable of real-time parameter adjustment for improved accuracy.

Contribution

It presents a new evolving neural network model, eT2QFNN, that adapts in real-time to non-stationary environments for RFID localization in manufacturing settings.

Findings

eT2QFNN achieves accuracy comparable to state-of-the-art methods.

The model automatically adjusts its parameters and rules during operation.

Numerical results demonstrate effective localization in dynamic shopfloor conditions.

Abstract

{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.