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：

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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。