Incremental Topology Generated for ANN: A Case Study on a Helicopter’s Flight Simulation

  • Pedro FernandesJr.
  • Alexandre C. B. Ramos
  • Danilo Pereira Roque
  • Marcelo Santiago de Sousa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)

Abstract

This paper presents a method for the development of artificial neural networks (ANN) that consists in the use of a search space algorithm to adjust the components of an ANN’s initial structure, based on the performance obtained by different network configurations. Also, it is possible to represent an ANN’s structure as a genetic sequence, which enables directly loading a corresponding genetic sequence to instantly generate and run a previously trained ANN. This paper also shows some results obtained by different ANNs developed by this method, which demonstrate its features by analyzing its accuracy and trueness. As an example for application of this method, a case study is presented for a specific flight simulation, using data obtained from a helicopter’s flight dynamics simulator for ANN training. Helicopter flight dynamics is a relevant study, for it can be used, for example, to provide precise data to a flight simulator, which implies in an important issue for pilot training, and subsequently, this type of application may help reducing the probability of pilot’s faults in a real flight mission. Finally, some considerations are made about the work shown in this paper as the results, discussions and conclusions are presented.

Keywords

ANN Artificial Intelligence Helicopter 

Notes

Acknowledgment

The author thank UNIFEI for research support, and CAPES and FAPEMIG for financial support. Confirmation number: 270.

References

  1. 1.
    X. You, M. Ji, H. Han, The effects of risk perception and flight experience on airline pilots’ locus of control with regard to safety operation behaviors. Accid. Anal. Prev. 9, 131–139 (2013)CrossRefGoogle Scholar
  2. 2.
    J.S. Melo, M.S.R. Tadeucci. A atividade aérea e uso de simulador de voo, XIV Encontro Latino Americano, (2010)Google Scholar
  3. 3.
    J. Ryder, T. Santarelli, J. Scolaro, J. Hicinbothom, W. Zachary, Comparison of cognitive model uses in intelligent training systems. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 4, 374–377 (2000)CrossRefGoogle Scholar
  4. 4.
    L.R. Ribeiro, Plataforma de Testes para Sistemas de Piloto Automático Utilizando Matlab/Simulink e Simulador de voo X-Plane (Instituto Tecnológico de Aeronáutica, São José dos Campos, 2011)Google Scholar
  5. 5.
    W.C. Lu, R.M. Faye, A.C.B. Ramos, J. Slama, F. Mora-Camino, Neural inversion of flight guidance dynamics. ISDA 2007 6, 190–195 (2007)Google Scholar
  6. 6.
    A.K. Ghosh, S.C. Raisinghani, Frequency-domain estimation of parameters from flight data using neural networks. J. Guid. Control. Dyn. 6, 525–530 (2001) CrossRefGoogle Scholar
  7. 7.
    D.J. Linse, R.F. Stengel, Identification of aerodynamic coefficients using computational neural networks. J. Guid. Control. Dyn. 8, 1018–1025 (1993)MathSciNetCrossRefGoogle Scholar
  8. 8.
    X. Yao, Evolving artificial neural networks. Proc. IEEE 25, 1423–1447 (1999)Google Scholar
  9. 9.
    K.O. Stanley, R. Miikkulainen, Efficient evolution of neural network topologies. Proc. 2002 Cong. Evol. Comput. 6, 1757–1762 (2002)Google Scholar
  10. 10.
    S. Cussat-Blanc, K. Harrington, J. Pollack, Gene regulatory network evolution through augmenting topologies. IEEE Trans. Evol. Comput. 15, 823–837 (2015) CrossRefGoogle Scholar
  11. 11.
    S.S. Cunha Jr., M.S. de Sousa, D.P. Roque, A.C.B. Ramos, P. Fernandes Jr., Dynamic simulation of the flight behavior of a rotary-wing aircraft. Inf. Tech. N. Gener. 13, 1087–1099 (2016)Google Scholar
  12. 12.
    P.V.M. Simplício, Helicopter Nonlinear Flight Control Using Incremental Nonlinear Dynamic Inversion (Delft University of Technology, Delft, 2011)Google Scholar
  13. 13.
    G.D. Padfield, Helicopter Flight Dynamics, 2nd edn. (John Wiley & Sons, Hoboken, 2008)Google Scholar
  14. 14.
    I. Lima, C.A.M. Pinheiro, F.A.O. Santos, Inteligência Artificial, 1st edn. (Elsevier, Brasil, 2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Pedro FernandesJr.
    • 1
  • Alexandre C. B. Ramos
    • 1
  • Danilo Pereira Roque
    • 2
  • Marcelo Santiago de Sousa
    • 2
  1. 1.Federal University of Itajubá, Mathematics and Computng InstituteItajubáBrazil
  2. 2.Federal University of Itajubá, Mechanical Engineering InstituteItajubáBrazil

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