On H ∞  Filtering in Feedforward Neural Networks Training and Pruning

  • He-Sheng Tang
  • Song-Tao Xue
  • Rong Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


An efficient training and pruning method based on H∞ filtering algorithm is proposed for Feedforward neural networks (FNNs). A FNNs’ weight importance measure linking up prediction error sensitivity obtained from H ∞  filtering training and a weight salience based pruning technique are derived. The results of extensive experimentation indicate that the proposed method provides better pruning results during the training process of the network without losing its generalization capacity, also provides a robust global optimization training algorithm for given arbitrary network structures.


Extend Kalman Filter Recursive Less Square Pruning Method Generalization Capacity Recursive Less Square Algorithm 
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

  • He-Sheng Tang
    • 1
  • Song-Tao Xue
    • 2
  • Rong Chen
    • 1
  1. 1.Research Institute of Structural Engineering and Disaster ReductionTongji UniversityShanghaiChina
  2. 2.Department of Architecture, School of Science and EngineeringKinki UniversityHigashi Osaka CityJapan

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