Parameter Identification Inverse Problems of Partial Differential Equations Based on the Improved Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{conf/cnhpca/ChenLC15,
  author =       "Yan Chen and Kangshun Li and Zhangxin Chen",
  title =        "Parameter Identification Inverse Problems of Partial
                 Differential Equations Based on the Improved Gene
                 Expression Programming",
  booktitle =    "High Performance Computing and Applications: Third
                 International Conference, HPCA 2015",
  year =         "2015",
  editor =       "Jiang Xie and Zhangxin Chen and Craig C. Douglas and 
                 Wu Zhang and Yan Chen",
  volume =       "9576",
  series =       "Lecture Notes in Computer Science",
  pages =        "218--227",
  address =      "Shanghai, China",
  month =        jul # " 26-30",
  publisher =    "Springer",
  note =         "Revised Selected Papers",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, partial differential equation,
                 inverse problems, thomas algorithm",
  bibdate =      "2017-05-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/https://doi.org/10.1007/978-3-319-32557-6_24;
                 DBLP,
                 http://dblp.uni-trier.de/db/conf/cnhpca/cnhpca2015.html#ChenLC15",
  DOI =          "doi:10.1007/978-3-319-32557-6_24",
  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