Prediction of Automotive Seat Response Based on IPSO-BP Neural Network

To enhance the predictive accuracy between seat structural parameters and crash performance, a hybrid model was constructed by coupling an Improved Particle Swarm Optimization (IPSO) algorithm with a Back Propagation Neural Network (BPNN). First, a finite element model for front and rear impact of automotive seats was established based on experimental data, and the model’s accuracy was verified. Subsequently, simulations were conducted, and the results were analyzed. The Energy Absorption Mass Ratio method was used to screen the design variables, ultimately selecting 10 thickness variables and 9 material variables as design variables. Latin Hypercube Sampling was employed to divide the dataset into a testing set and a training set. Then, the Particle Swarm Optimization (PSO) was enhanced with Levy flights and a local mutation strategy, utilizing the IPSO algorithm to optimize the initial weights and thresholds of the BPNN, resulting in the establishment of the IPSO-BPNN predictive model. The results indicate that the proposed IPSO-BPNN shows significant advantages in predicting the output of automotive seats.