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Designing and Modelling of Delta Wing Genetic-Based Prediction Model

Author

Listed:
  • Arun M. P.

    (Dept of Mechanical Engineering, MET'S School of Engineering, India)

  • Satheesh M.

    (Mechanical and Motor Vehicle Division, Bahrain Training Institute, Bahrain)

  • J. Edwin Raja Dhas

    (Noorul Islam Centre for Higher Education, India)

Abstract

The designing and modeling of delta wing is one of the interesting topics. A number of researchers has contributed different works on modeling the same. This paper comes out with a new delta wing modeling with the inclusion of optimization concept. The obtained data from the investigation is integrated and given as the input to the classifier for predicting the drag and lift coefficients. This paper uses neural network (NN) classifier for predicting the drag and lift coefficients. Moreover, the weight of the NN is optimized using a proposed genetic algorithm. After the implementation, the performance of proposed model is compared to other conventional methods like individual adaptive genetic algorithm (IAGA-NN), deterministic adaptive genetic algorithm (DAGA-NN), self-adaptive genetic algorithm (SAGA-NN), genetic algorithm (GA-NN), gradient descendent (GD-NN), and Levenberg masquerade (LM-NN), respectively, in terms of drag and lift coefficient.

Suggested Citation

  • Arun M. P. & Satheesh M. & J. Edwin Raja Dhas, 2021. "Designing and Modelling of Delta Wing Genetic-Based Prediction Model," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 12(1), pages 159-183, January.
    • Handle: RePEc:igg:jaci00:v:12:y:2021:i:1:p:159-183
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