Two new techniques are proposed to enhance the estimation abilities of the conventional neural network (NN) method for its application to the fitness function estimation of aerodynamic shape optimization with the genetic algorithm (GA). The first technique is pre-processing the training data in order to increase the training accuracy of the multi-layer perceptron (MLP) approach. The second technique is a new structure for the network to improve its quality through a modified growing and pruning method. Using the proposed techniques, one can obtain the best estimations from the NN with less computational time. The new methods are applied for optimum design of a transonic airfoil and the results are compared with those obtained from the accurate Computational Fluid Dynamics (CFD) fitness evaluator and also with the conventional MLP NN approach. The numerical experiments show that using the new method can reduce the computational time significantly while achieving the improved accuracy.
Timnak, N., Jahangirian, A., & Seyyedsalehi, S. A. (2017). An Optimum Neural Network for Evolutionary Aerodynamic Shape Design. Scientia Iranica, 24(5), 2490-2500. doi: 10.24200/sci.2017.4308
MLA
N. Timnak; A. Jahangirian; S. A. Seyyedsalehi. "An Optimum Neural Network for Evolutionary Aerodynamic Shape Design". Scientia Iranica, 24, 5, 2017, 2490-2500. doi: 10.24200/sci.2017.4308
HARVARD
Timnak, N., Jahangirian, A., Seyyedsalehi, S. A. (2017). 'An Optimum Neural Network for Evolutionary Aerodynamic Shape Design', Scientia Iranica, 24(5), pp. 2490-2500. doi: 10.24200/sci.2017.4308
VANCOUVER
Timnak, N., Jahangirian, A., Seyyedsalehi, S. A. An Optimum Neural Network for Evolutionary Aerodynamic Shape Design. Scientia Iranica, 2017; 24(5): 2490-2500. doi: 10.24200/sci.2017.4308
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