Prediction of thickness of blasting scatter water belt based on GA-BP and PSO-BP

Zhiwen Liu; Wenhao Cai; Liangliang Liu; Tao Bian

doi:10.1117/12.2684778

19 October 2023 Prediction of thickness of blasting scatter water belt based on GA-BP and PSO-BP

Zhiwen Liu, Wenhao Cai, Liangliang Liu, Tao Bian

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270920 (2023) https://doi.org/10.1117/12.2684778
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

In this paper, a method for predictive analysis is proposed based on BP neural network optimized by genetic algorithm and BP neural network optimized based on particle swarm algorithm. By optimizing the initial weight and threshold of the neural network by the algorithm, showing the error between the predicted value and the real value, and comparing the mean squared error of the three, the MSE of the GA-BP prediction model is reduced by 56.53% compared with the BP neural network prediction model. The MSE of the PSO-BP network predictive model was reduced by 50.00%. The results show that this method can accurately predict the thickness of the blasting protective water belt, which provides a new and reliable method for the study of controlling blasting flying objects.

Citation Download Citation

Zhiwen Liu, Wenhao Cai, Liangliang Liu, and Tao Bian "Prediction of thickness of blasting scatter water belt based on GA-BP and PSO-BP", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270920 (19 October 2023); https://doi.org/10.1117/12.2684778

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