Prediction of failure pressure of corrosion pipelines based on RS-PSO-ELM

doi:10.16265/j.cnki.issn1003-3033.2021.03.004

Abstract

Abstract: In order to improve prediction accuracy of corrosion pipelines' failure pressure and simplify its calculation process, a prediction model based on RS, PSO and ELM was proposed. Key factors that affected failure pressure were extracted in a way of attribute reduction, PSO was selected to optimize input weight and hidden layer deviation of ELM, and normalized core index data were computed in calculation. The results show that prediction of the model is basically consistent with actual values, its mean square error (MSE) is reduced to 0.255 compared with single ELM model, and absolute mean error is reduced to 0.32 compared with other assessment models of failure pressure.

Key words: rough set (RS), particle swarm optimization(PSO), extreme learning machine (ELM), corrosion pipelines, failure pressure

CLC Number:

X944.4

LUO Zhengshan, TIAN Peiqi. Prediction of failure pressure of corrosion pipelines based on RS-PSO-ELM[J]. China Safety Science Journal, 2021, 31(3): 28-34.

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