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Aircraft Icing Information Processing Based on Artificial Neural Network

Author: WangJun
Tutor: ZhangJie
School: Huazhong University of Science and Technology
Course: Detection Technology and Automation
Keywords: Icing sensor Modeling Neural Networks Positional Incremental
CLC: V244.15
Type: Master's thesis
Year: 2007
Downloads: 243
Quote: 0
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Abstract


60 years people have been to solve the problem of aircraft icing , anti-icing system is improved , but the accidents caused by icing remains a threat to flight safety . Technical level of aircraft icing protection needed a substantial increase in aircraft icing detection technology is critical , especially ahead of the icing detection , security measures is of great significance for the pilots to take early . This paper introduces the basic features of the the flat film icing sensor , leads to the dynamic characteristics of the sensor temperature characteristics ii icing latent heat of the sensor temperature rises . Thesis, the identification of latent heat as the breakthrough point to discuss the liquid-solid phase transition characteristics of the latent heat of the two programs on the mathematical modeling and neural network modeling and feasibility analysis , detailing the BP artificial neural network modeling the process as well as the optimization of the design of the network , through the identification of the characteristics of the sensor temperature of the sensor system behavior level modeling . With the research the MATLAB platform designed the BP neural network, and the position , the two data samples incremental program testing contrast , given their respective strengths and weaknesses . The simulation results show that the neural network modeling program is feasible , the generalization ability of the neural network , fault tolerance characteristics so that it can not in the training set of data can produce reasonable output . Both information processing capabilities make neural networks can solve some of the complex issues of general approach is not yet able . Icing icing sensor with BP neural network recognition only preliminary work , the combination of feature extraction unsupervised networks the aerial flight online learning will be the direction of future development .

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CLC: > Aviation, aerospace > Aviation > Aircraft instrumentation,avionics, flight control and navigation > Protection, life - saving equipment and technology > Protective equipment > Anti-icing and demisting equipment
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