Lateral jet interaction model identification based on genetic programming

Authors:
Shi-Ming Chen;Yun-Feng Dong;Xiao-Lei Wang
Affiliations:
School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing, P.R. China;School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing, P.R. China;Science and Technology on Space System Simulation Laboratory, Beijing Simulation Center, Beijing, P.R. China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Year:
2011

Citing 4
Cited 0

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications

Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Stochastic nonlinear system identification using multi-objective multi-population parallel genetic programming

CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

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 optimize 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 30% than multivariable regression method.