Quantitative Analysis of Structural Parameters Importance of Helical Temperature Microfiber Sensor by Artificial Neural Network

Quantitative Analysis of Structural Parameters Importance of Helical Temperature Microfiber Sensor by Artificial Neural Network

With the assistance of the evaluation algorithms based on the well-performed backpropagation neural network (BPNN), we quantitatively analyze the importance of the structural parameters of the supported helical microfiber (HMF) temperature sensor. The relative output intensities of HMF sensor at dif...

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Bibliographic Details
Main Authors: Juan Liu, Minghui Chen, Hang Yu, Jinjin Han, Hongyi Jia, Zhili Lin, Zhijun Wu, Jixiong Pu, Xining Zhang, Hao Dai
Format: article
Language:EN
Published: IEEE 2021
Subjects:
Helical microfiber
temperature sensor
quantitative contribution of structural parameters
backpropagation neural network
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Online Access:https://doaj.org/article/8c7793bcc0a54932b21f588ace0db1f6
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https://doaj.org/article/8c7793bcc0a54932b21f588ace0db1f6

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