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International Journal of Aerospace Engineering
Volume 2011 (2011), Article ID 247294, 7 pages
Research Article

Efficient Neural Network Modeling for Flight and Space Dynamics Simulation

Aerospace Engineering Department, Cairo University, Cairo 12613, Egypt

Received 26 March 2011; Revised 19 July 2011; Accepted 9 August 2011

Academic Editor: C. B. Allen

Copyright © 2011 Ayman Hamdy Kassem. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.