Title: Electromagnetic pulse response prediction of intelligent wireless sensor based on NARX

Authors: Cui Hao; Wenbai Chen; Hao Wu; Changjian Jiang

Addresses: School of Automation, Beijing Information Science and Technology University, Beijing 100192, China ' School of Automation, Beijing Information Science and Technology University, Beijing 100192, China ' School of Automation, Beijing Information Science and Technology University, Beijing 100192, China ' School of Automation, Beijing Information Science and Technology University, Beijing 100192, China

Abstract: Artificial neural network algorithm can represent all functions at any accuracy through learning the observed data and training parameters. Compared with conventional methods such as analytical methods, which could be limited in accuracy, or numerical modelling methods, which could be time-consuming, the artificial neural network algorithm is attractive for providing fast and accurate answers in the modelling of electromagnetic pulse response prediction of intelligent wireless sensors. According to the characteristics of input and output, non-linear autoregressive with external input (NARX) neural network was chosen in this paper. It can reveal that the current output value depends on its own previous output values and the input values. In order to verify the accuracy of the model, the electromagnetic pulse experiments of intelligent wireless sensors with protection circuit and without protection circuit were done. The results showed that the input-output curve estimated by the NARX neural network model is in good agreement with the experiments results. After two groups of simulation, the NARX model has high fitting ability, which suggests that the NARX model has good generalisation ability.

Keywords: electromagnetic pulse; intelligent wireless sensor; modelling; NARX neural network; signal line; transient voltage suppressor.

DOI: 10.1504/IJWMC.2021.119056

International Journal of Wireless and Mobile Computing, 2021 Vol.21 No.1, pp.1 - 10

Received: 28 Sep 2020
Accepted: 12 Nov 2020
Published online: 19 Nov 2021 *

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