Lateral Jet Interaction Model Identification Based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Shi-Ming Chen and Yun-Feng Dong and Xiao-Lei Wang",
  title =        "Lateral Jet Interaction Model Identification Based on
                 Genetic Programming",
  booktitle =    "Proceedings Third International Conference on
                 Artificial Intelligence and Computational Intelligence
                 (AICI 2011) Part {I}",
  year =         "2011",
  editor =       "Hepu Deng and Duoqian Miao and Jingsheng Lei and 
                 Fu Lee Wang",
  volume =       "7002",
  series =       "Lecture Notes in Computer Science",
  pages =        "484--491",
  address =      "Taiyuan, China",
  month =        sep # " 24-25",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, lateral jet,
                 model identification, missile",
  isbn13 =       "978-3-642-23880-2",
  DOI =          "doi:10.1007/978-3-642-23881-9_63",
  size =         "8 pages",
  abstract =     "Precise lateral jet interaction models are required
                 for missiles' blending control strategies. Because of
                 the complicated flow field, the interaction models are
                 multivariable, complex and coupled. Traditional
                 aerodynamics coefficients model identification used
                 Maximum-likelihood estimation to adjust the parameters
                 of the postulation model, but it is not good at dealing
                 with complex nonlinear models. A genetic programming
                 (GP) method is proposed to identify the interaction
                 model, which not only can optimise the parameters, but
                 also can identify the model structure. The interaction
                 model's inputs are altitude, mach number, attack angle
                 and fire number of jets in wind channel experiment
                 results, and its output is interaction force
                 coefficient. The fitness function is root mean square
                 error. Select suitable function set and terminal set
                 for GP, then use GP to evolve model automatically. The
                 identify process with different reproduced probability;
                 crossover probability and mutation probability are
                 compared. Results shows that GP's result error is
                 decrease 30percent than multi-variable regression
  bibdate =      "2011-09-30",
  bibsource =    "DBLP,

Genetic Programming entries for Shi-Ming Chen Yun-Feng Dong Xiao-Lei Wang