当前位置: X-MOL 学术Math. Probl. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines
Mathematical Problems in Engineering Pub Date : 2020-09-17 , DOI: 10.1155/2020/3681032
Xiaoxu Chen 1 , Linyuan Wang 1 , Zhiyu Huang 1, 2
Affiliation  

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.

中文翻译:

基于主成分分析的动态模糊神经网络的燃气管道内腐蚀速率预测

针对天然气管道内部腐蚀速率非线性变化的特点,人工神经网络很容易陷入局部最优。本文提出了一种结合主成分分析(PCA)算法和动态模糊神经网络(D-FNN)的模型来解决上述问题。主成分分析算法用于降维和特征提取,动态模糊神经网络模型用于进行预测。对PCA-D-FNN的研究是利用来自真实管道的腐蚀数据进一步完成的,并将结果与​​人工神经网络,模糊神经网络和D-FNN模型进行了比较。结果证明了该模型和算法对内部腐蚀速率预测的有效性。
更新日期:2020-09-18
down
wechat
bug