Research on Corrosion Rate Prediction of Buried Pipeline Based on KPCA-Improved PSO-BP Neural Network Model
Author:
Affiliation:
1. East China University of Science and Technology,School of Mechanical Engineering,Shanghai,China,200237
2. East China University of Science and Technology,School of Resources and Environmental Engineering,Shanghai,China,200237
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10246450/10246476/10246699.pdf?arnumber=10246699
Reference10 articles.
1. Predictive Model Based on Genetic Algorithm-Neural Network for Fatigue Performances of Pre-Corroded Aluminum Alloys
2. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine
3. A PSO based approach: Scout particle swarm algorithm for continuous global optimization problems
4. Artificial neural network models for predicting condition of offshore oil and gas pipelines
5. The negative binomial distribution as a model for external corrosion defect counts in buried pipelines
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