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Speeding up the Training of Lattice-Ladder Multilayer Perceptrons
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2002 (English)Report (Other academic)
Abstract [en]

A lattice-ladder multilayer perceptron (LLMLP) is an appealing structure for advanced signal processing in a sense that it is nonlinear, possesses infinite impulse response and stability monitoring of it during training is simple. However, even moderate implementation of LLMLP training hinders the fact that a lot of storage and computation power must be allocated. In this paper we deal with the problem of computational efficiency of LLMLP training algorithms that are based on computation of gradients, e.g., backpropagation, conjugate-gradient or Levenberg-Marquardt.The paper aims to explore most computationally demanding calculations---computation of gradients for lattice (rotation)parameters. Here we find and propose to use for training of several LLMLP architectures a simplest in terms of storage and number of delay elements computation of exact gradients, assuming that the coefficients of the lattice-ladder filter are held stationary.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2002. , 30 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2417
Keyword [en]
Gradient adaptive lattice algorithms, lattice–ladder filter, lattice ladder multilayer perceptron, training, estimation, adaptation, backpropagation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55909ISRN: LiTH-ISY-R-2417OAI: oai:DiVA.org:liu-55909DiVA: diva2:316622
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-03Bibliographically approved

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Automatic ControlThe Institute of Technology
Control Engineering

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf