Intelligent Built-in Test (BIT) for More-Electric Aircraft Power System Based on Hybrid Generalized LVQ Neural Network

  • Zhen Liu
  • Hui Lin
  • Xin Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


This paper proposes a hybrid neural network model based on the Generalized Learning Vector Quantization(GLVQ) learning algorithm and applies this proposed method to the BIT system of More-Electric Aircraft Electrical Power System (MEAEPS). This paper first discusses the feasibility of application unsupervised neural networks to the BIT system and the representative Generalized LVQ (GLVQ) neural network is selected due to its good performance in clustering analysis. Next, we adopt a new form of loss factor to modify the original GLVQ algorithm in order to make it more suitable for our application. Since unsupervised networks cannot distinguish the similar classes, we add a LVQ layer to the GLVQ network to construct a hybrid neural network model. Finally, the proposed method has been applied to the intelligent BIT system of the MEAEPS, and the results show that the proposed method is promising to improve the performance of the BIT system.


Fault Diagnosis Fault Mode Hybrid Network Winner Node Unsupervised Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhen Liu
    • 1
  • Hui Lin
    • 1
  • Xin Luo
    • 1
  1. 1.College of AutomationNorthwestern Polytechnical UniversityXi’anChina

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