Earticle

현재 위치 Home

Aircraft Engine Fuel Flow Prediction Using Process Neural Network

첫 페이지 보기
  • 발행기관
    보안공학연구지원센터(IJCA) 바로가기
  • 간행물
    International Journal of Control and Automation SCOPUS 바로가기
  • 통권
    Vol.7 No.3 (2014.03)바로가기
  • 페이지
    pp.53-62
  • 저자
    Yu Guangbin, Gang Ding, Lin Lin, Zhao Xingfu, Zhao Yang
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A218415

※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

원문정보

초록

영어
Monitoring the aircraft engine fuel flow is critical to the flight safety and the aircraft maintenance economy. Aim at predicting the aircraft engine fuel flow accurately and quickly, an aircraft engine fuel flow prediction method based on the process neural network is proposed in this paper. The learning speed of the existing learning algorithms (e.g. BP learning algorithm) for process neural network is too slow for the practical application. A Levenberg-Marquardt learning algorithm based on the expansion of the orthogonal basis functions is developed to raise the adaptability of the process neural network to the real problems. Finally, the proposed prediction method with the corresponding learning algorithm is utilized to predict the fuel flow of some aircraft engine, the results indicate that the proposed prediction method seems to perform well and appears suitable for using as an aircraft engine health condition monitoring tool, and the comparative results also indicate that the Levenberg-Marquardt learning algorithm has a faster learning convergence speed and a higher prediction accuracy than the BP learning algorithm.

목차

Abstract
 1. Introduction
 2. Time Series Prediction Model Based on Process Neural Network
  2.1. Process Neuron Model
  2.2. Time Series Prediction Model Based on Process Neural Network
 3. LM Learning Algorithm Based on Orthogonal Basis Functions
 4. Application Test
 5. Conclusions
 Acknowledgements
 References

키워드

Aircraft engine fuel flow Process neural network Time series prediction Aircraft engine health condition monitoring Orthogonal basis function

저자

  • Yu Guangbin [ Harbin Institute of Technology, Heilongjiang, P.R. China, 150001 ]
  • Gang Ding [ Harbin Institute of Technology, Heilongjiang, P.R. China, 150001 ] Corresponding author
  • Lin Lin [ Harbin Institute of Technology, Heilongjiang, P.R. China, 150001 ]
  • Zhao Xingfu [ Harbin Institute of Technology, Heilongjiang, P.R. China, 150001 ]
  • Zhao Yang [ Harbin Institute of Technology, Heilongjiang, P.R. China, 150001 ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    보안공학연구지원센터(IJCA) [Science & Engineering Research Support Center, Republic of Korea(IJCA)]
  • 설립연도
    2006
  • 분야
    공학>컴퓨터학
  • 소개
    1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업

간행물

  • 간행물명
    International Journal of Control and Automation
  • 간기
    월간
  • pISSN
    2005-4297
  • 수록기간
    2008~2016
  • 십진분류
    KDC 505 DDC 605

이 권호 내 다른 논문 / International Journal of Control and Automation Vol.7 No.3

    피인용수 : 0(자료제공 : 네이버학술정보)

    함께 이용한 논문 이 논문을 다운로드한 분들이 이용한 다른 논문입니다.

      페이지 저장