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Characteristic Prediction of Wind Tunnel Tests using Learning from Examples
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 35 - Number 6 |
| Year of Publication: 2011 |
| Authors: Dr. R. Malmathanraj, T. Francis Tyson |
10.5120/4403-6114
|
Dr. R. Malmathanraj, T. Francis Tyson . Characteristic Prediction of Wind Tunnel Tests using Learning from Examples. International Journal of Computer Applications. 35, 6 ( December 2011), 5-14. DOI=10.5120/4403-6114
Abstract
This paper presents a systematic neural network approach based on the concept for Learning from Examples for the prediction of aerodynamic characteristics from the Wind tunnel test data. Aerodynamic coefficients are modeled as functions of angle of attack, Normal force coefficient, Mach number, and Lift force Coefficients. The training data which is fed as the input to the neural network is derived from wind tunnel test measurements and numerical simulations. In this paper, a comparative study of the efficiency of neural network prediction based on LFE(Learning from Examples) for different architectures and training dataset sizes is presented. The results of the prediction reflect the sensitivity of the architecture and training dataset size. For a training set of 136 data points and a training set with Mach number ranging from 0.6 to 3, the Generalized Regression Neural network (GRNN) constantly out-performed the Radial basis function neural network and Backpropagation network regression model in time effectiveness. The objective of this paper is to demonstrate that the neural network approach based on the concept of learning from examples is a fast and reliable way for predicting aerodynamic coefficients.
References
- R. Malmathanraj and S. ThamaraiSelvi , 2008. Prediction of Aerodynamic Characteristics Using Neural Networks. Asian Journal of Information Technology, 7: 19-26.
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams. 1986. Learning internal representations by error propagation. In Parallel Distributed Processing, pages 318-362, D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, Editors, The MIT Press, Cambridge, MA.
- S. M. Weiss and I. Kapouleas. 1989. An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pages 781-787, Detroit, MI, August 20-25.
- C. Moallemi. 1991. Classifying cells for cancer diagnosis using neural networks IEEE Expert, pages 8-12, December.
- R. Mooney, J. Shavlik, G. Towell, and A. Gove. 1989. An experimental comparison of symbolic and connectionist learning algorithms. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pages 775-780, Detroit, MI, August 20-25.
- A. S. Hussain, X. Yu, and R. D. Johnson. 1991. Application of neural network computing in pharmaceutical product development. Pharm. Res., 8:1248-1252.
- P. Veng-Pedersen and N. B. Modi. 1992. Neural networks in pharmacodynamic modeling. Is current modeling practice of complex kinetic systems at a dead end, J. Pharmacokinet. Biopharm., 20:397-412.
- R. J. Erb. 1992. Neural network computation of nonlinear pharmacological effects from concentration of drug in the central compartment independent of equilibrium status. Pharm. Res., 9:293.
- Y. Suzuki, T. Aoyama, and H. Ichikawa. 2007. Neural networks applied to pharmaceutical problems. J. Med. Chem., 33(9):2583-2590 (1990). Tavel, P. Modeling and Simulation Design. AK Peters Ltd.
- Bodor, N.; Huang, M.-J. 1991. Neural Network Studies. 1. Estimation of the Aqueous Solubilty of Organic Compounds. J. Am. Chem. Soc.113, 9480-9483
- RajkumarThirumalainambi and Jorge Bardina, "Training data requirement for a neural network to predict aerodynamic coefficients", Proc. SPIE 5102, 92 (2003); doi:10.1117/12.486343.
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