Airworthiness Evaluation Model Based on Fuzzy Neural Network

  • Jie-Ru Jin
  • Peng Wang
  • Yang ShenEmail author
  • Kai-Xi Zhang
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


Based on fuzzy neural network, this study explores the quantifying calculation problem of airworthiness of specific rescue operations. Aiming at the problem of quantification assessment of flightworthiness for rescue flight operation, this study starts from the perspective of the mechanical properties of the aircraft itself, and use a fuzzy neural network model for rescue operations of airworthiness evaluation modeling. The rescue historical data of the EC-135 helicopter model is used for model training, in order to form a quantitative model of airworthiness assessment for flight operation. On the one hand, the quantitative output results provide qualitative guidance for aircraft’s competency, and on the other hand, it provides a basis for comparing the advantages and disadvantages of multitask allocation schemes. Optimization of the whole system task allocation effect is formed through the best way of individual utility. Aiming at the heterogeneous problem for rescue system, this paper introduces the concept of “task ability vector”, quantitative representing the ability of heterogeneous aircrafts and requirements of mission. The comprehensive ability of quantitative calculation of multi-aircrafts alliance is discussed, as well as the single and multi-aircrafts cooperative task ability.


Rescue task allocation Fuzzy neural network Aviation emergency rescue 



Supported by the National Key Research and Development Program of China(Grant No.2016YFC08022603).


  1. 1.
    X. Deng, X. Wang, Incremental learning of dynamic fuzzy neural networks for accurate system modeling. Fuzzy Sets Syst. 160(7), 972–987 (2009)CrossRefGoogle Scholar
  2. 2.
    A.F. Gobi, W. Pedrycz, The potential of fuzzy neural networks in the realization of approximate reasoning engines. Fuzzy Sets Syst. 8(22), 2954–2973 (2006)CrossRefGoogle Scholar
  3. 3.
    C.F. Juang, T.M. Chen, Birdsong recognition using prediction-based recurrent neural fuzzy networks. Neurocomputing 71(1–3), 121–130 (2007). C.F. Juang, L.T. Chen, Moving object recognition by a shape-based neural fuzzy network. Neurocomputing 71(13), 2937–2949 (2008)Google Scholar
  4. 4.
    M. Singh, S. Srivastava, M. Hanmandlu et al., Type-2 fuzzy wavelet networks (T2FWN) for system identification using fuzzy differential and Lyapunov stability algorithm. Appl. Soft Comput. 9(3), 977–989 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jie-Ru Jin
    • 1
  • Peng Wang
    • 2
  • Yang Shen
    • 1
    Email author
  • Kai-Xi Zhang
    • 1
  1. 1.College of Economics and ManagementNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Haifeng General Aviation Technology Co., LtdBeijingChina

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