Thickness regression model of ultra-thin amorphous alloy based on improved PSO-GRNN

Zhen Guo; Jianyan Tian; Bo Li; Zhien Li; Gaopeng Han; Xianhe Liu

doi:10.1117/12.2646954

7 September 2022 Thickness regression model of ultra-thin amorphous alloy based on improved PSO-GRNN

Zhen Guo, Jianyan Tian, Bo Li, Zhien Li, Gaopeng Han, Xianhe Liu

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123290P (2022) https://doi.org/10.1117/12.2646954
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

The temperature and pressure in the transition ladle are two important factors that affect the thickness of ultra-thin amorphous alloy, and there is a complex nonlinear relationship between the two factors and the thickness. To solve the problem that the control model cannot be determined in the process of thickness control of ultra-thin amorphous alloy, a regression model of ultra-thin amorphous alloy thickness based on improved PSO-GRNN is proposed. And the regression model based on GRNN takes temperature and pressure as input and thickness as output, which is a model that describes the nonlinear relationship between them. Based on the standard PSO algorithm, this paper introduces crossover and mutation operations, and the inertia weight of the PSO algorithm is improved. The smoothing parameter of GRNN is optimized by the improved PSO algorithm. Combined with the production field data, the model is simulated and analyzed. Comparing the method in this paper with BP, RBF, GRNN, and standard PSO-GRNN models, the results show that the method in this paper can effectively improve the accuracy of the model and can be used as the control model of the ultra-thin amorphous alloy thickness control system.

Citation Download Citation

Zhen Guo, Jianyan Tian, Bo Li, Zhien Li, Gaopeng Han, and Xianhe Liu "Thickness regression model of ultra-thin amorphous alloy based on improved PSO-GRNN", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123290P (7 September 2022); https://doi.org/10.1117/12.2646954

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KEYWORDS

Data modeling

Particle swarm optimization

Particles

Genetic algorithms

Optimization (mathematics)

Statistical modeling

Data processing

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