Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios

Joint Authors

Wang, Hong-Gang
Wang, Shan-Shan
Pan, Ruo-Yu
Pang, Sheng-Li
Liu, Xiao-Song
Luo, Zhi-Yong
Zhou, Sheng-Pei

Source

Complexity

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-12-29

Country of Publication

Egypt

No. of Pages

15

Main Subjects

Philosophy

Abstract 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.

American Psychological Association (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

Modern Language Association (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

American Medical Association (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

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1144742