MATEC Web Conf.
Volume 363, 20225th International Conference on Advances in Materials, Machinery, Electronics (AMME 2022)
|Number of page(s)||6|
|Published online||29 August 2022|
THOR 50th Dummy Neck Calibration Analysis Based on Bi-LSTM Neural Network
CATARC Automotive Test Center (Tianjin) Co., Ltd., Tianjin, China
In this paper, a neural network model is established based on the neck calibration data of the Thor50th crash test dummy using a bi-directional long and short-term memory (Bi-LSTM) neural network algorithm. The model input is the factors affecting the neck calibration test, and the output is the maximum value of My in the neck calibration test, and the accuracy of the model is calibrated by comparing it with the actual calibration data. The accuracy and suitability of the Bi-LSTM neural network model is further verified by comparing with the radial basis (RBF) neural network algorithm.
Key words: Thor 50th Dummy / Neck calibration test / Bi-LSTM neural network / RBF neural network
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.