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Airworthiness Evaluation Model Based on Fuzzy Neural Network

In: Smart Service Systems, Operations Management, and Analytics

Author

Listed:
  • Jie-Ru Jin

    (Nanjing University of Aeronautics and Astronautics)

  • Peng Wang

    (Haifeng General Aviation Technology Co., Ltd)

  • Yang Shen

    (Nanjing University of Aeronautics and Astronautics)

  • Kai-Xi Zhang

    (Nanjing University of Aeronautics and Astronautics)

Abstract

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 networkFuzzy 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.

Suggested Citation

  • Jie-Ru Jin & Peng Wang & Yang Shen & Kai-Xi Zhang, 2020. "Airworthiness Evaluation Model Based on Fuzzy Neural Network," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), Smart Service Systems, Operations Management, and Analytics, pages 183-194, Springer.
  • Handle: RePEc:spr:prbchp:978-3-030-30967-1_17
    DOI: 10.1007/978-3-030-30967-1_17
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