Volume 29 Issue 6
Dec.  2020
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YANG Zhijun, MAO Lei, GAN Jianhou, et al., “Performance Analysis and Prediction of Double-Server Polling System Based on BP Neural Network,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1046-1053, 2020, doi: 10.1049/cje.2020.09.005
Citation: YANG Zhijun, MAO Lei, GAN Jianhou, et al., “Performance Analysis and Prediction of Double-Server Polling System Based on BP Neural Network,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1046-1053, 2020, doi: 10.1049/cje.2020.09.005

Performance Analysis and Prediction of Double-Server Polling System Based on BP Neural Network

doi: 10.1049/cje.2020.09.005
Funds:  This work is supported by the National Natural Science Foundation of China (No.61461054, No.61461053 and No.61862068).
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  • Corresponding author: GAN Jianhou (corresponding author) received the B.S. degree in computer science education and M.S. degree in mathematics from Yunnan Normal University, China, in 1998 and 2004. He received Ph.D. degree in computational metallurgy from Kunming University of Science and Technology, China, in 2016. He is the vice director of Key Laboratory of Educational Information for Nationalities, Yunnan Normal University, Kunming, P.R. China. His current research interests include knowledge engineering and educational information for nationalities.(Email:ganjh@ynu.edu.cn)
  • Received Date: 2019-10-11
  • Publish Date: 2020-12-25
  • To solve the poor performance of the single-server polling system in high traffic and the complex analysis of the multi-server polling system, a synchronous double-server polling system is proposed, and its performance is analyzed using a Backpropagation (BP) neural network prediction algorithm. Experimental data are processed and analyzed, and a three-layer multiinput single-output BP network model is constructed to predict the performance of the polling system under different arrival rates of information packets. In the prediction stage, first, the data are processed and the average queue length under different information arrival rates is used to form a sequence. Subsequently, a multiinput single-output BP neural network is constructed for prediction. Experimental results show that the algorithm can accurately predict the performance of the double-server polling system, thereby facilitating research regarding polling systems.
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