Application of an optimization artificial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system
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 carts 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.
optimization artificial immune network particle swarm optimization fuzzy neural network radio frequency identification