A Comprehensive Evaluation Method Based on PCA and BP Neural Network

2012 ◽  
Author(s):  
Xiaodan Xie ◽  
Bin Xia ◽  
Jun Yu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Comprehensive Evaluation Method

Safety Assessment of Transmission Grid Based on Fuzzy Comprehensive Evaluation and BP Neural Network

2010 ◽  
Vol 439-440 ◽  
pp. 528-533
Author(s):  
Yuan Sheng Huang ◽  
Wei Fang ◽  
Cheng Fang Tian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Safety Assessment ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Transmission Grid ◽  
Comprehensive Evaluation Method ◽  
System Method ◽  
Grid Based

In the practice of safety assessment on transmission grid, there is the variation degree of many indexes which can not be accurately described, and fuzzy comprehensive evaluation method can reflect the safety degree of every element. In addition, the combination use of BP neural network and expert system method can determine impact extent of assessment factors on safety of transmission grid and the weight of each factor relative to safety of transmission grid. Therefore, the paper proposes the safety assessment of transmission grid based on BP neural network and fuzzy comprehensive evaluation. Finally, an example is used to prove the method is high precision and practical.

A New Method to Predict Gas Production Based on Fuzzy BP Artificial Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 688-691
Author(s):  
Ran Zhang ◽  
Jun Zhou ◽  
Cheng Yong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Gas Production ◽  
Gas Well ◽  
Well Productivity ◽  
Qualitative Factors ◽  
Productivity Prediction

BP neural network has been successfully used in the gas well productivity prediction, but as a result of neural network is sensitive to the number of input parameters, we had to ignore some factors that is less important to the gas well productivity. In addition, the existing various productivity prediction method cannot consider the influence of some important qualitative factors. This article integrated the advantages of fuzzy comprehensive evaluation and BP neural network, fuzzy comprehensive evaluation method is used to construct the BP neural network's input matrix, and BP neural network learning function is used to solve the connection weights, so as to achieve the aim of predicting gas production. This method not only can consider as many factors influence on gas well production, ut also can consider qualitative factors, so the forecast results of the new model are more realistically close to the actual production situation of reservoirs.

A Multi-Factor Comprehensive Evaluation Method for Pipeline Integrity

2019 ◽  
Author(s):  
Jing Yang ◽  
Xiaolin Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Analysis ◽  
Evaluation Method ◽  
Residual Life ◽  
Comprehensive Evaluation ◽  
Comprehensive Evaluation Method ◽  
Grey Correlation ◽  
Grey Correlation Analysis ◽  
Artificial Neural

Abstract Pipeline integrity management is widely used as an effective means for pipeline safety management, in which integrity evaluation is an important part. To some extent, pipeline integrity can be interpreted as the safety condition of the pipeline, while safety is an eternal topic for pipeline operators. In numerous recent studies, the evaluation of pipeline integrity generally focuses on the evaluation of remaining strength and/or residual life, which is based on the defect size such as corrosion, dents, etc., obtained during inspection. However, pipeline integrity is not only related to the pipe body, all factors that may threaten the operation safety of the pipe should be considered, including the pipe body, ancillary facilities, the pipe security system, and the surrounding environment, etc.. Although some comprehensive models have been established recently to assess pipeline condition, there still exist limitations for practical application, such as quantification of integrity and complexity of analysis. Therefore this paper presents the development of a comprehensive integrity evaluation method based on multi-factor analysis. The method is developed by an integrated application of fuzzy mathematics, grey correlation analysis theory, and the artificial neural network technique. After establishing integrity evaluating indexes, fuzzy analysis is used to quantify and classify pipeline integrity, and grey correlation analysis to screen key influence indicators. Then a comprehensive predictive evaluation model can be generated using large amount of relevant sample data based on the artificial neural network technique. In the end of the paper, a simple case is applied to validate feasibility of this comprehensive integrity evaluation method. The comprehensive evaluation method is expected to be applied to determine the condition of pipeline integrity, and to grade and rank the integrity condition of pipes, so as to assist and optimize pipeline maintenance decision for pipeline operators.

Sign in / Sign up

Export Citation Format

Share Document