DocumentCode :
2688224
Title :
Construction of surrogate model ensembles with sparse data
Author :
Chen, Dingding ; Zhong, Allan ; Gano, John ; Hamid, Syed ; De Jesus, Orlando ; Stephenson, Stan
Author_Institution :
Halliburton Energy Services, Carrollton
fYear :
2007
fDate :
25-28 Sept. 2007
Firstpage :
244
Lastpage :
251
Abstract :
Construction of neural network ensembles (NNE) with sparse data requires comprehensive performance measure, multi-stage validation and usually a large member size. This paper presents a hybrid method which takes a selective optimization approach and is characterized with several novel features. First, candidate ensembles are widely explored using a multi-objective genetic algorithm. Secondly, the best local ensembles registered with each distinct objective weighting are determined based on the multi-stage validation results. Finally, a large global ensemble is formed by combining several local ensembles and virtually evaluated in the voids of possible parameter space. The demonstration of the proposed method is presented in a case study in which sparse data from FEA simulations are used to construct NNE for expandable pipe design, a novel application in oil and gas industry.
Keywords :
design engineering; genetic algorithms; mechanical engineering computing; neural nets; pipes; expandable pipe design; multiobjective genetic algorithm; multistage validation; neural network ensembles; objective weighting; parameter space; performance measure; selective optimization approach; sparse data; surrogate model ensemble; Evolutionary computation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1339-3
Electronic_ISBN :
978-1-4244-1340-9
Type :
conf
DOI :
10.1109/CEC.2007.4424478
Filename :
4424478
Link To Document :
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