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A Rapid Two-Step Learning Algorithm for Spline Activation Function Neural Networks with the Application on Biped Gait Recognition

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

A fast two-stage learning algorithm is proposed to construct and optimize the weights of spline activation function neural networks (SAFNN). Feedforward network is firstly trained by back propagate (BP) algorithm, and then errors are applied to generate new neurons in hidden layers. A rapid dynamic updating algorithm is introduced to modify the new weights. Generalization capability and approximation precision are ensured by the two steps respectively. Simulation results on biped gaits demonstrate improvements in these two capabilities and of learning speed with comparison to traditional BP in SFANN and common NN.

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References

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Author information

Authors and Affiliations

  1. State Key Laboratory of Intelligent Technology and Systems, Computer Science and Technology Department, Tsinghua University, Beijing, China, 100084

    Lingyun Hu & Zengqi Sun

Authors
  1. Lingyun Hu
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  2. Zengqi Sun
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Hu, L., Sun, Z. (2004). A Rapid Two-Step Learning Algorithm for Spline Activation Function Neural Networks with the Application on Biped Gait Recognition. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_49

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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