Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines

Xiaoxu Chen, Linyuan Wang and Zhiyu Huang

Mathematical Problems in Engineering, 2020, vol. 2020, 1-9

Abstract:

Aiming at the characteristics of the nonlinear changes in the internal corrosion rate in gas pipelines, and artificial neural networks easily fall into a local optimum. This paper proposes a model that combines a principal component analysis (PCA) algorithm and a dynamic fuzzy neural network (D-FNN) to address the problems above. The principal component analysis algorithm is used for dimensional reduction and feature extraction, and a dynamic fuzzy neural network model is utilized to perform the prediction. The study implementing the PCA-D-FNN is further accomplished with the corrosion data from a real pipeline, and the results are compared among the artificial neural networks, fuzzy neural networks, and D-FNN models. The results verify the effectiveness of the model and algorithm for inner corrosion rate prediction.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3681032

DOI: 10.1155/2020/3681032

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