Optimizing projectile aerodynamic parameter identification of kernel extreme learning machine based on improved Dung Beetle Optimizer algorithm

Accurately obtaining the aerodynamic parameters of a projectile is crucial for improving aircraft performance, optimizing design and control, and enhancing weapon strike accuracy. In this study, the Dung Beetle Optimization (DBO) algorithm was utilized to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KELM). To fully exploit the performance of the DBO algorithm and enhance identification accuracy, four improvement measures were proposed for the DBO algorithm. The enhanced DBO algorithm, known as the Improved Dung Beetle Optimization (IDBO) algorithm, was developed and validated using the CEC2017 test function. The proposed IDBO-KELM algorithm was applied to the aerodynamic parameter identification of projectiles. Comparative analysis with results from ELM, IDBO-ELM, and other models revealed that single ELM struggles to accurately identify aerodynamic parameters. While the identification results of IDBO-ELM were 3-5 orders of magnitude higher than those of ELM, a certain gap still existed between the identification results and the true values in the highly nonlinear transonic region. Notably, the IDBO-KELM model yielded the best identification results, with an overall improvement of 1-2 orders of magnitude compared to IDBO-ELM, effectively reducing errors in identification within the transonic region. Experimental findings demonstrate that the proposed algorithm combines the advantages of high accuracy and stability.