Application of an optimization artificial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system

作者：Kuo R J^*; Hung S Y; Cheng W C

来源：Information Sciences, 2014, 262: 78-98.

DOI：10.1016/j.ins.2013.10.035

• Radio frequency identification
• Optimization artificial immune network
• Particle swarm optimization
• Fuzzy neural network

Because of the advantages of radio frequency identification (RFID), this study uses an integrated optimization artificial immune network (Opt-aiNET) and a particle swarm optimization (PSO)-based fuzzy neural network (IOAP-FNN) to determine the relationship between the RFID signals and the position of a picking cart for an RFID-based positioning system. The results for the three benchmark functions indicate that the proposed IOAP-FNN performs better than the other algorithms. In addition, model evaluation results also demonstrate that the proposed algorithm really can predict the picking cart%26apos;s position more accurately. Moreover, unlike artificial neural networks, the proposed approach allows much easier interpretation of the training results, since they are in the form of fuzzy IF-THEN rules.

• 出版日期2014-3-20