Paper Info
Title
Study on Parameter Distribution in Structure Reliability Analysis: Machine Learning Algorithm and Application
Abstract
The discrimination of parameter probability distribution type is the key to structure reliability analysis. A support vector machine (SVM) intelligent recognition model of probability distribution law is presented aiming at traditional method disadvantage. The intelligent recognition model of probability distribution is constructed by SVM algorithm realization, network design and feature extraction, inward stress probability distribution type of a stem structural member is recognized by the model, recognition result is Weibull distribution, SVM has a good generalization ability and clustering ability by comparison between network recognition result and regression analysis, the experiment result shows total recognition rate achieved 98.25%, it provides a good new method for structure reliability analysis.
Year
DOI
Venue
2009
10.1109/WKDD.2009.169
WKDD
Keywords
Field
DocType
parameter probability distribution type,inward stress probability distribution,weibull distribution,network recognition result,total recognition rate,parameter distribution,intelligent recognition model,recognition result,structure reliability analysis,probability distribution law,machine learning algorithm,svm algorithm realization,probability distribution,machine learning,regression analysis,svm,support vector machine,support vector machines,statistical distributions,network design,feature extraction,reliability
Data mining,Regression analysis,Computer science,Weibull distribution,Probability distribution,Artificial intelligence,Cluster analysis,Network planning and design,Pattern recognition,Support vector machine,Algorithm,Feature extraction,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
1
Authors
2
Name
Order
Citations
PageRank
Yi Wan132.78
Yangu Zhang222.40