An Improved BP Algorithm Based on Global Revision Factor and Its Application to PID Control

  • Lin Lei
  • Houjun Wang
  • Yufang Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


To improve BP algorithm in overcoming the local minimum problem and accelerating the convergence speed, a new improved algorithm based on global revision factor of BP neural network is presented in this paper. The basic principle is to improve the formula of weight adjusting used momentum back propagation. A global revision factor is added in the weight value adjusting formula of momentum BP. Faster learning speed is obtained by adaptive adjusting this factor. The new BP algorithm is compared with other improved BP algorithms on many aspects. Simulation and applications for complex nonlinear function approximation, neural PID parameter tuning indicates that it has better training speed and precision than momentum back propagation and adaptive learning rate.


Hide Layer Hide Layer Node Adaptive Adjust Local Minimum Problem Adaptive Learning Rate 
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

  • Lin Lei
    • 1
  • Houjun Wang
    • 1
  • Yufang Cheng
    • 1
  1. 1.School of Automation and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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