Partial least squares learning regression for backpropagation network

Author

Hsiao, Tzu-Chien ; Lin, Chii-Wann ; Chiang, Hui-Hua Kenny

Author_Institution

Nat. Yang-Ming Univ., Taipei, Taiwan

Volume

2

fYear

2000

fDate

2000

Firstpage

975

Abstract

The relationship between the partial least squares (PLS) regression and the general delta rule algorithm is investigated. This PLS regression can be adopted as an efficient pre-learning method for backpropagation (BP) network. The PLS regression based BP network (PLSBP network) has better capacity during training phase. Aided 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

Keywords

backpropagation; feedforward neural nets; least squares approximations; statistical analysis; argument error minimum; backpropagation network; cost function; efficient prelearning method; feedforward ANN; general delta rule algorithm; initial weights decision; logistic map; network simulation; optimal minimum; partial least squares learning regression; two-layer network; Artificial neural networks; Backpropagation algorithms; Chemical analysis; Cost function; Learning systems; Least squares approximation; Least squares methods; Logistics; Optimization methods; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE

Conference_Location

Chicago, IL

ISSN

1094-687X

Print_ISBN

0-7803-6465-1

Type

conf

DOI

10.1109/IEMBS.2000.897885

Filename

897885