Pattern recognition of particle tracks using principal component analysis and artificial neural network

被引：9

作者：

Dutta, D

Mohanty, AK ^{[1
]}

Choudhury, RK

Chand, P

机构：

[1] Bhabha Atom Res Ctr, Div Nucl Phys, Mumbai 400085, India

[2] Bhabha Atom Res Ctr, Comp Div, Mumbai 400085, India

来源：

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT | 1998年 / 404卷 / 2-3期

关键词：

D O I：

10.1016/S0168-9002(97)01139-X

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

A new method is suggested for pattern recognition of particle tracks based on a combined approach of both artificial neural network (ANN) and principal component analysis (PCA). It is seen that in high multiplicity environment, neither the PCA nor the ANN method is satisfactory when used separately as a track classifier. Best performance is achieved when the data are preprocessed using PCA technique, before it is fed to the backpropagated neural network. (C) 1998 Elsevier Science B.V. All rights reserved.

引用

页码：445 / 454

页数：10