Parameter Identification Inverse Problems of Partial Differential Equations Based on the Improved Gene Expression Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8194
- @InProceedings{conf/cnhpca/ChenLC15,
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author = "Yan Chen and Kangshun Li and Zhangxin Chen",
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title = "Parameter Identification Inverse Problems of Partial
Differential Equations Based on the Improved Gene
Expression Programming",
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booktitle = "High Performance Computing and Applications: Third
International Conference, HPCA 2015",
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year = "2015",
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editor = "Jiang Xie and Zhangxin Chen and Craig C. Douglas and
Wu Zhang and Yan Chen",
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volume = "9576",
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series = "Lecture Notes in Computer Science",
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pages = "218--227",
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address = "Shanghai, China",
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month = jul # " 26-30",
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publisher = "Springer",
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note = "Revised Selected Papers",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, partial differential equation,
inverse problems, thomas algorithm",
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bibdate = "2017-05-23",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/cnhpca/cnhpca2015.html#ChenLC15",
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DOI = "doi:10.1007/978-3-319-32557-6_24",
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abstract = "Traditionally, solving the parameter identification
inverse problems of partial differential equations
encountered many difficulties and insufficiency. In
this paper, we propose an improved GEP (Gene Expression
Programming) to identify the parameters in the reverse
problems of partial differential equations based on the
self-adaptation, self-organization and self-learning
characters of GEP. This algorithm simulates a
parametric function itself of a partial differential
equation directly through the observed values by fully
taking into account inverse results caused by noises of
a measured value. Modelling is unnecessary to add
regularization in the modeling process aiming at
special problems again. The experiment results show
that the algorithm has good noise-immunity. In case
there is no noise or noise is very low, the identified
parametric function is almost the same as the original
accurate value; when noise is very high, good results
can still be obtained, which successfully realizes
automation of the parameter modeling process for
partial differential equations.",
- }
Genetic Programming entries for
Yan Chen
Kangshun Li
Zhangxin (John) Chen
Citations