Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13027
Title: Monitoring the performance of conveyor system using radio frequency identification in manufacturing environment : a recurrent neural network and genetic algorithm-based approach
Authors: Singh, V
Sarwar, F
Chan, FTS 
Tiwari, MK
Keywords: conveyor systems
genetic algorithm
neural network
RFID
simulation
supply chain management
Issue Date: 2012
Publisher: Taylor & Francis
Source: International journal of computer integrated manufacturing, 2012, v. 25, no. 7, p. 551-564 How to cite?
Journal: International journal of computer integrated manufacturing 
Abstract: A number of new approaches to address the identification issues have been proposed recently, but due to the highly integrated nature of passive radio frequency identification (RFID) tags, it is difficult to evaluate them in real-world scenarios. A recurrent neural network-based hybrid approach with training through genetic algorithm has been proposed to model the performance of the RFID system with received power at the reader in the radio propagation channel as the implementable performance index. Target system is a conveyor system delivering multiple products. A method to deploy RFID technology has been developed and illustrated for smoothening flow on a conveyor. Although various analytical models have been proposed earlier, they fail to accurately predict the performance of RFID system. Proposed method incorporates various factors presented in the industrial environment, while only a few are considered in the analytical model. Such an integrated approach is a genuine extension of a previous model where only neural network model was tested to embrace the system's performance. A comparative study has been carried out to establish the better performance of proposed approach. The model proposed may be helpful to aid in the research area of simulation of RFIDs on computer for reflecting numerous factors in modelling for RFID system performance without sacrificing predictability.
URI: http://hdl.handle.net/10397/13027
ISSN: 0951-192X
EISSN: 1362-3052
DOI: 10.1080/0951192X.2011.646309
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