THOR 50th Dummy Neck Calibration Analysis Based on Bi-LSTM Neural Network

Abstract

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.