Partial least squares learning regression for backpropagation network

Tzu-Chien Hsiao*, C. W. Lin, H. H.K. Chiang

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

The relationship between the Partial Least Squares (PLS) regression and the General Delta Rule (GDR) algorithm is investigated in this report. This PLS regression can be adopted as an efficient pre-learning method for Backpropagation (BP) network. The PLS regression based BP network (named as PLSBP network) has better capacity during training phase. Abided by the statistical concept of the PLS regression, the cost function of this network is guaranteed to be an optimal minimum. The logistic map for network simulation is provided as an example in this report.

Original languageEnglish
Pages (from-to)975-977
Number of pages3
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume2
DOIs
StatePublished - 1 Dec 2000
Event22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, United States
Duration: 23 Jul 200028 Jul 2000

Keywords

  • BP network
  • Optimal minimum
  • PLS regression

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