Hybrid of artificial immune system and particle swarm optimization-based support vector machine for Radio Frequency Identification-based positioning system

This study intends to propose a hybrid of artificial immune system (AIS) and particle swarm optimization (PSO)-based support vector machine (SVM) (HIP-SVM) for optimizing SVM parameters, and applied it to radio frequency identification (RFID)-based positioning system. In order to evaluate HIP-SVM's capability, six benchmark data sets, Australian, Heart disease, Iris, Ionosphere, Sonar and Vowel, were employed. The computational results showed that HIP-SVM has better performance than AIS-based SVM and PSO-based SVM. HIP-SVM was also applied to classify RSSI for indoor positioning. The experiment results indicated that HIP-SVM can achieve highest accuracy compared to those of AIS-SVM and PSO-SVM. It demonstrated that RFID can be used for storing information and in indoor positioning without additional cost.

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