Improved BP Neural Network Algorithm for Predicting Structural Parameters of Mirrors

Abstract

Addressing the nonlinear correlations between input variables and output responses, in addition to the time-consuming nature of finite element analysis in mirror design, this study introduces an enhanced back-propagation (BP) neural network (BR-TLDBO-BPNN) employing Bayesian regularization and an optimized dung beetle algorithm. This novel approach facilitates rapid and efficient parameter estimations, significantly reducing the computational overhead. Utilizing an integrated analysis platform, the study obtained training and test samples, and the BR-TLDBO-BPNN model is used to predict the reflector’s mass and root mean square (RMS). The optimization mathematical model is built, and the nonlinear planning function (fmincon) is utilized to solve the problem and find an ideal set of structural parameters. The outcomes demonstrate that the prediction model is accurate enough to predict the mirror characteristics to optimize the mirror structural parameters. Empirical validation demonstrates that the proposed model achieves an over 99% accuracy in predicting mirror characteristics against finite element simulations. As a result, the BR-TLDBO-BPNN algorithm studied in this article not only broadens the application scope of neural networks, but also provides a new practical technique for engineering design.