Error Estimation for Material Simulation Data Based on Hybrid Learning Algorithm

doi:10.16182/j.issn1004731x.joss.201810030

Abstract

Abstract: In order to obtain high quality material simulation data from Density Functional Theory material calculation software package, a modeling method based on BP neural network was proposed to build model estimating the error of material simulation data. A novel hybrid algorithm combining simple particle swarm optimization algorithm that excludes speed item with BP algorithm, also referred to tsPSO-BP, was proposed to optimize the connection weights of the BP neural network. The hybrid learning algorithm not only makes use of strong global searching ability of the PSO, but also strong local searching ability of the BP algorithm. The BP neural network model was trained using tsPSO-BP on the dataset of experimental and calculation data of elastic constants for binary alloys with cubic crystal system, and the results show that the prediction accuracy of the error of C₁₁, C₁₂ and C₄₄ were 88.19%, 87.83% and 88.26%, respectively.

Key words: error estimation for material simulation data, neural network, particle swarm optimization, BP algorithm, hybrid learning algorithm

CLC Number:

Wang Juan, Yang Xiaoyu, Wang Zongguo, Ren Jie, Zhao Xushan. Error Estimation for Material Simulation Data Based on Hybrid Learning Algorithm[J]. Journal of System Simulation, 2018, 30(10): 3843-3853.

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