Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios
المؤلفون المشاركون
Wang, Hong-Gang
Wang, Shan-Shan
Pan, Ruo-Yu
Pang, Sheng-Li
Liu, Xiao-Song
Luo, Zhi-Yong
Zhou, Sheng-Pei
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-29
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
With the rapid development of Internet of Things technology, RFID technology has been widely used in various fields.
In order to optimize the RFID system hardware deployment strategy and improve the deployment efficiency, the prediction of the RFID system identification rate has become a new challenge.
In this paper, a neighborhood rough set and random forest (NRS-RF) combination model is proposed to predict the identification rate of an RFID system.
Firstly, the initial influencing factors of the RFID system identification rate are reduced using neighborhood rough set theory combined with the principle of heuristic attribute reduction of neighborhood weighted dependency, thus obtaining a kernel factor subset.
Secondly, a random forest prediction model is established based on the kernel factor subset, and a confusion matrix is established using out-of-bag (OOB) data to evaluate the prediction results.
The test is conducted under the constructed RFID experimental environment, whose results showed that the model can predict the identification rate of the RFID system in a fast and efficient way, and the classification accuracy can reach 90.5%.
It can effectively guide the hardware deployment and communication parameter protocol setting of the system and improve the system performance.
Compared with BP neural network (BPNN) and other prediction models, NRS-RF has shorter prediction time and faster calculation speed.
Finally, the validity of the proposed model was verified by the RFID intelligent archives management platform.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Hong-Gang& Wang, Shan-Shan& Pan, Ruo-Yu& Pang, Sheng-Li& Liu, Xiao-Song& Luo, Zhi-Yong…[et al.]. 2020. Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios. Complexity،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1144742
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Hong-Gang…[et al.]. Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios. Complexity No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1144742
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Hong-Gang& Wang, Shan-Shan& Pan, Ruo-Yu& Pang, Sheng-Li& Liu, Xiao-Song& Luo, Zhi-Yong…[et al.]. Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios. Complexity. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1144742
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1144742
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر