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Fast Orthogonal Neural Networks

  • Bartłomiej Stasiak
  • Mykhaylo Yatsymirskyy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)

Abstract

The paper presents a novel approach to the construction and learning of linear neural networks based on fast orthogonal transforms. The orthogonality of basic operations associated with the algorithm of a given transform is used in order to substantially reduce the number of adapted weights of the network. Two new types of neurons corresponding to orthogonal basic operations are introduced and formulas for architecture-independent error backpropagation and weights adaptation are presented.

Keywords

Discrete Cosine Transform Discrete Fourier Transform Digital Signal Processing Conjugate Gradient Method Basic Operation 
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

  • Bartłomiej Stasiak
    • 1
  • Mykhaylo Yatsymirskyy
    • 1
  1. 1.Institute of Computer ScienceTechnical University of ŁódźŁódźPoland

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