Research on Fire-Engine Pressure Balance Control System Based Upon Neural Networks

  • Xiao-guang Xu
  • Hong-da Shen
Part of the Communications in Computer and Information Science book series (CCIS, volume 98)


The pressure produced by the water coming out of the fire-engine pump outlet is controlled by the rotate speed of the fire pump. However, this RS is controlled through fire-engine accelerator voltage which is controlled by the ECU. In order to control and keep the fire-engine pressure balanced, it is necessary to take pressure, rotate speed and current rate as input parameters and control voltage as output parameter through BP neural network control system. Related researches indicate that BP neural network is appropriate for building the system whose target is to keep the pressure balanced. And, some modification can be done to the standard BP neural network algorithm. These modified BP neural network algorithms are BP neural network with momentum factors and self-adapting learning speed which can improve the response speed and performance of this control system dramatically.


pressure balance neural networks momentum factors 


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  1. 1.
    Feng, H., Lin, D.S.: China’s situation and the development direction of fire-engine. Fire Technique and Products Information (11), 69–71 (2003) 何锋, 董松林. 我国消防车的现状及发展方向. 消防技术与产品信息 (11) 69-71 (2003)Google Scholar
  2. 2.
    Ho, K.L., Hsu, Y. Y., Yang, C. C.: ST LF using a multilayer neural net work with an adaptive learning algorithm. IEEE Trans. on PS 7(1), 141–149 (1992)Google Scholar
  3. 3.
    Parlos, A.G.: An accelerated learning algorithm for multi player perceptron networks. IEEE Trans. on Neural Networks 5(3), 86–88 (1994)CrossRefGoogle Scholar
  4. 4.
    Hong, L., Qiu-fang, T., Hui, L.: Application of BP a lgor ithm in the ba lance of underactuated manipulator. Journal of Beijing Institute of Machinery 24(3), 17–21 (2009) 厉虹,田秋芳,李慧. BP算法在欠驱动机械臂平衡控制中的应用. 北京信息科技大学学报. 24(3), 17–21 (2009)Google Scholar
  5. 5.
    Shouren, H., Shaobo, Y., Kui, D.: Introduction to Aritificial Neural Networks. National University of Defense Technology Press, Changsha (1996) 胡守仁,余少波, 戴葵. 神经网络导论. 长沙: 国防科技大学出版社 (March 1999)Google Scholar
  6. 6.
    Rodriguez, C.: A modular neural network approach to fault diagnosis. IEEE Trans. on N ns 7(2), 326–340 (1996)CrossRefGoogle Scholar
  7. 7.
    Ham, F.M., Kostanic, I.: Principles of neurocomputing for science and engineering. McGraw-Hill, New York (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Xiao-guang Xu
    • 1
  • Hong-da Shen
    • 1
  1. 1.Department of Electrical EngineeringAnhui Polytechnic UniversityWuhuChina

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