Abstract
A modelling and simulation problem is considered for longitudinal motion of a maneuverable aircraft that is viewed as a nonlinear controlled dynamical system under multiple and diverse uncertainties. This problem is solved by utilizing semi-empirical neural network based approach that combines theoretical domain-specific knowledge with training tools of artificial neural network field. Semi-empirical approach allows for a substantial accuracy improvement over traditional purely empirical models such as the Nonlinear AutoRegressive neural network with eXogenous inputs (NARX). It also provides solution to system identification problem for aerodynamic characteristics of an aircraft, such as the coefficients of aerodynamic axial and normal forces, as well as the pitch moment coefficient. Representative training data set is obtained using an automatic procedure which synthesizes control actions that provide a sufficiently dense coverage of the region of change in the values of variables describing the simulated system. Neural network model learning efficiency is further improved by the use of special weighting scheme for individual training samples. Obtained simulation results confirm the efficiency of proposed simulation approach.
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Egorchev, M., Tiumentsev, Y. (2018). Neural Network Semi-empirical Modeling of the Longitudinal Motion for Maneuverable Aircraft and Identification of Its Aerodynamic Characteristics. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research. NEUROINFORMATICS 2017. Studies in Computational Intelligence, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-66604-4_10
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DOI: https://doi.org/10.1007/978-3-319-66604-4_10
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