Title: A decoupling algorithm for three-dimensional electric field sensors based on extreme learning machines optimised by bat algorithm

Authors: Wei Zhao; Zhizhong Li

Addresses: School of Intelligent Manufacturing, Huzhou College, Huzhou, 313000, China ' National Defence Engineering College, Army Engineering University of PLA, Nanjing, 210007, China

Abstract: During measuring the spatial electric field intensity using a three-dimensional electric field sensor, due to the electric field components' coupling effect caused by the electric field distortion, a certain coupling error exists in the electric field intensity components measurement. Aiming at the problem of insufficient decoupling accuracy of the traditional extreme learning machine method, an optimised extreme learning machine method based on the combination of maximum inter-class variance and the bat algorithm is proposed to decouple the three-dimensional electric field sensor. The bat algorithm optimised the extreme learning machine method's optimal initial weight and threshold. The maximum inter-class variance method was used to analyse the inherent coupling characteristics of the sensor. The coupling effect was classified according to the varying coupling contribution degree. The traditional extreme learning machine decoupling network was extended. The calibration experiments and decoupling calculations show that the extreme learning machine algorithm optimised by the bat algorithm and maximum inter-class variance can effectively reduce the error, which is between the electric field components obtained by the model calculation and the actual electric field components, and can effectively reduce the interference generated by the inter-dimensional coupling effect of the sensor, and further improve the measurement accuracy of the electric field intensity.

Keywords: bat algorithm; decoupling; extreme learning machine; ELM; electric filed sensor.

DOI: 10.1504/IJSNET.2026.152036

International Journal of Sensor Networks, 2026 Vol.50 No.2, pp.73 - 84

Received: 13 May 2025
Accepted: 22 May 2025
Published online: 04 Mar 2026 *

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