Integration of magnified gradient function and weight evolution with deterministic perturbation into back-propagation

Sin Chun Ng; Chi Chung Cheung; Shu Hung Leung

doi:10.1049/el:20030277

Integration of magnified gradient function and weight evolution with deterministic perturbation into back-propagation

Sin Chun Ng, Chi Chung Cheung, Shu Hung Leung

Research output: Journal article publication › Journal article › Academic research › peer-review

1 Citation (Scopus)

Abstract

An approach for the improved performance of back-propagation (BP) learning systems, based on the integration of magnified gradient function and weight evolution with deterministic perturbation, was discussed. In this regard, the regression and character recognition problems were considered. The simulation results, in terms of the convergence rate and global convergence, showed that the integrated approach always outperformed other traditional methods.

Original language	English
Pages (from-to)	447-448
Number of pages	2
Journal	Electronics Letters
Volume	39
Issue number	5
DOIs	https://doi.org/10.1049/el:20030277
Publication status	Published - 6 Mar 2003
Externally published	Yes

ASJC Scopus subject areas

Electrical and Electronic Engineering

More information

10.1049/el:20030277

Cite this

@article{c2ddd4e5ac304daeb8184de0432d96dd,

title = "Integration of magnified gradient function and weight evolution with deterministic perturbation into back-propagation",

abstract = "An approach for the improved performance of back-propagation (BP) learning systems, based on the integration of magnified gradient function and weight evolution with deterministic perturbation, was discussed. In this regard, the regression and character recognition problems were considered. The simulation results, in terms of the convergence rate and global convergence, showed that the integrated approach always outperformed other traditional methods.",

author = "Ng, {Sin Chun} and Cheung, {Chi Chung} and Leung, {Shu Hung}",

year = "2003",

month = mar,

day = "6",

doi = "10.1049/el:20030277",

language = "English",

volume = "39",

pages = "447--448",

journal = "Electronics Letters",

issn = "0013-5194",

publisher = "John Wiley & Sons Inc.",

number = "5",

}

TY - JOUR

T1 - Integration of magnified gradient function and weight evolution with deterministic perturbation into back-propagation

AU - Ng, Sin Chun

AU - Cheung, Chi Chung

AU - Leung, Shu Hung

PY - 2003/3/6

Y1 - 2003/3/6

N2 - An approach for the improved performance of back-propagation (BP) learning systems, based on the integration of magnified gradient function and weight evolution with deterministic perturbation, was discussed. In this regard, the regression and character recognition problems were considered. The simulation results, in terms of the convergence rate and global convergence, showed that the integrated approach always outperformed other traditional methods.

AB - An approach for the improved performance of back-propagation (BP) learning systems, based on the integration of magnified gradient function and weight evolution with deterministic perturbation, was discussed. In this regard, the regression and character recognition problems were considered. The simulation results, in terms of the convergence rate and global convergence, showed that the integrated approach always outperformed other traditional methods.

UR - http://www.scopus.com/inward/record.url?scp=0037422119&partnerID=8YFLogxK

U2 - 10.1049/el:20030277

DO - 10.1049/el:20030277

M3 - Journal article

AN - SCOPUS:0037422119

SN - 0013-5194

VL - 39

SP - 447

EP - 448

JO - Electronics Letters

JF - Electronics Letters

IS - 5

ER -

Integration of magnified gradient function and weight evolution with deterministic perturbation into back-propagation

Abstract

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this