Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR)

Abstract

Little work has been carried out to predict the comfort of aircraft seats, a component in close contact with the human body during travel. In order to more accurately predict the nonlinear and complex relationship between subjective and objective evaluations of comfort, this paper proposes a prediction method based on the Improved Particle Swarm Algorithm (IPSO) and optimized Support Vector Machine Regression (SVR). Focusing on the problems of the too-fast convergence and low accuracy of the traditional particle swarm algorithm (PSO), the improved particle swarm algorithm (IPSO) is obtained by linearly decreasing the dynamic adjustments of inertia weight ω, self-learning factor c1, and social factor c2; then, the penalty parameter C and kernel function parameter σ of SVR are optimized by the IPSO algorithm, and the comfort prediction of IPSO-SVR is established. The prediction accuracy of IPSO-SVR was 94.00%, the root mean square error RMSE was 0.37, the mean absolute value error MAE was 0.32, and the goodness of fit R2 was 0.92. The results show that the optimized IPSO-SVR prediction model can more accurately predict seat comfort under different angles and backrest tilt angles and can provide reference and research value for related industries. The results show that the optimized nonlinear prediction model of IPSO-SVR has higher accuracy, and its prediction method is feasible and generalizable, meaning it can provide a reliable basis for the prediction of seat comfort under different angles and backrest inclinations, as well as providing reference and research value for related industries.