On-line training and pruning for recursive least square algorithms

Chi Sing Leung; Kwok Wo Wong; Pui Fai Sum; Lai Wan Chan

doi:10.1049/el:19961443

On-line training and pruning for recursive least square algorithms

Chi Sing Leung, Kwok Wo Wong, Pui Fai Sum, Lai Wan Chan

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

41 Citations (Scopus)

Abstract

The authors derive a pruning method for recursive least square (RLS) based algorithms. In this approach, training (on-line type) and pruning are combined. Simulation results showed that this approach is an effective training and pruning method for neural networks.

Original language	English
Pages (from-to)	2152-2153
Journal	Electronics Letters
Volume	32
Issue number	23
DOIs	https://doi.org/10.1049/el:19961443
Publication status	Published - 7 Nov 1996

Research Keywords

Least squares approximations
Neural networks

Access Output

10.1049/el:19961443

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@article{85b3dffe38d840ff9ea9dfe639a6f5a1,

title = "On-line training and pruning for recursive least square algorithms",

abstract = "The authors derive a pruning method for recursive least square (RLS) based algorithms. In this approach, training (on-line type) and pruning are combined. Simulation results showed that this approach is an effective training and pruning method for neural networks.",

keywords = "Least squares approximations, Neural networks",

author = "Leung, {Chi Sing} and Wong, {Kwok Wo} and Sum, {Pui Fai} and Chan, {Lai Wan}",

year = "1996",

month = nov,

day = "7",

doi = "10.1049/el:19961443",

language = "English",

volume = "32",

pages = "2152--2153",

journal = "Electronics Letters",

issn = "0013-5194",

publisher = "John Wiley & Sons",

number = "23",

}

TY - JOUR

T1 - On-line training and pruning for recursive least square algorithms

AU - Leung, Chi Sing

AU - Wong, Kwok Wo

AU - Sum, Pui Fai

AU - Chan, Lai Wan

PY - 1996/11/7

Y1 - 1996/11/7

N2 - The authors derive a pruning method for recursive least square (RLS) based algorithms. In this approach, training (on-line type) and pruning are combined. Simulation results showed that this approach is an effective training and pruning method for neural networks.

AB - The authors derive a pruning method for recursive least square (RLS) based algorithms. In this approach, training (on-line type) and pruning are combined. Simulation results showed that this approach is an effective training and pruning method for neural networks.

KW - Least squares approximations

KW - Neural networks

UR - http://www.scopus.com/inward/record.url?scp=0030574155&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0030574155&origin=recordpage

U2 - 10.1049/el:19961443

DO - 10.1049/el:19961443

M3 - RGC 21 - Publication in refereed journal

SN - 0013-5194

VL - 32

SP - 2152

EP - 2153

JO - Electronics Letters

JF - Electronics Letters

IS - 23

ER -

On-line training and pruning for recursive least square algorithms

Abstract

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this