Tag identification rate prediction based on neighborhood rough set and support vector machine

To optimize the hardware deployment of Radio Frequency Identification (RFID) system and improve the deployment efficiency, a RFID system identification rate prediction model based on Neighborhood Rough Set (NRS) and Support Vector Machines (SVM) theory was proposed. The initial influencing factors of RFID system identification rate were reduced by using the neighborhood rough set theory with the principle of minimal correlation and maximal dependency, and the kernel factor subset was obtained. The prediction model of support vector machine was established based on the kernel factor subset, and the prediction model of dynamic RFID system identification rate was constructed by using cross-validation and grid-search adaptive optimization model parameters. The prediction model was tested on the RFID experimental platform. The results showed that the prediction accuracy of the model could reach 92.89%, and the root mean square error value was 0.36. Compared with KNN-Naive Bayesian and other prediction models, the prediction time was shorter and the calculation speed was faster. The validity of the proposed model was verified by an application example of intelligent book management platform. © 2019, Editorial Department of CIMS. All right reserved.