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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
