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首页> 外文期刊>Information Sciences: An International Journal >Application of an optimization arti?cial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system
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Application of an optimization arti?cial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system

机译：优化人工免疫网络和基于粒子群优化的模糊神经网络在基于RFID的定位系统中的应用

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摘要

Because of the advantages of radio frequency identi?cation (RFID), this study uses an integrated optimization arti?cial 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's position more accurately. Moreover, unlike arti?cial neural networks, the proposed approach allows much easier interpretation of the training results, since they are in the form of fuzzy IF-THEN rules.
机译：由于射频识别（RFID）的优势，本研究使用集成的优化人工免疫网络（Opt-aiNET）和基于粒子群优化（PSO）的模糊神经网络（IOAP-FNN）确定RFID信号与基于RFID的定位系统的拣货车位置之间的关系。三个基准功能的结果表明，所提出的IOAP-FNN性能优于其他算法。此外，模型评估结果还表明，该算法确实可以更准确地预测拣货车的位置。此外，与人工神经网络不同，该提议的方法允许更容易地解释训练结果，因为它们采用模糊的IF-THEN规则的形式。

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