Radial basis functions applied to the classification of UV-visible spectra

被引：50

作者：

Pulido, A ^{[1
]}

Ruisánchez, I ^{[1
]}

Rius, FX ^{[1
]}

机构：

[1] Univ Rovira & Virgili, Dept Quim Analit & Quim Organ, Tarragona 430005, Spain

来源：

ANALYTICA CHIMICA ACTA | 1999年 / 388卷 / 03期

关键词：

radial basis functions; UV-visible; neural network;

D O I：

10.1016/S0003-2670(99)00082-3

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

This paper describes how to apply a neural network based in radial basis functions (RBFs) to classify multivariate data. The classification strategy was automatically implemented in a sequential injection analytical system. RBF neural network had some advantages over counterpropagation neural networks (CPNNs) when they are used in the same application: the classification error was reduced from 20% to 13%, the input variables (UV-visible spectra) did not have to be preprocessed and the training procedure was simpler. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：273 / 281

页数：9

共 28 条

[1] NEURAL NETWORKS AND THEIR APPLICATIONS [J].

BISHOP, CM .

REVIEW OF SCIENTIFIC INSTRUMENTS, 1994, 65 (06) :1803-1832

[2]

BOS A, 1993, THESIS U TWENTE ENSC

[3] ORTHOGONAL LEAST-SQUARES LEARNING ALGORITHM FOR RADIAL BASIS FUNCTION NETWORKS [J].

CHEN, S ;

COWAN, CFN ;

GRANT, PM .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1991, 2 (02) :302-309

[4] ROBUSTNESS ANALYSIS OF RADIAL BASE FUNCTION AND MULTILAYERED FEEDFORWARD NEURAL-NETWORK MODELS [J].

DERKS, EPPA ;

PASTOR, MSS ;

BUYDENS, LMC .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1995, 28 (01) :49-60

[5] Comment on a recent sensitivity analysis of radial base function and multi-layer feed-forward neural network models - Response [J].

Derks, EPPA ;

Pastor, MSS ;

Buydens, LMC .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1996, 34 (02) :299-301

[6] Comment on a recent sensitivity analysis of radial base function and multi-layer feed-forward neural network models [J].

Faber, K ;

Kowalski, BR .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1996, 34 (02) :293-297

[7] NONLINEAR MULTIVARIATE CALIBRATION USING PRINCIPAL COMPONENTS REGRESSION AND ARTIFICIAL NEURAL NETWORKS [J].

GEMPERLINE, PJ ;

LONG, JR ;

GREGORIOU, VG .

ANALYTICAL CHEMISTRY, 1991, 63 (20) :2313-2323

[8] MINIMAL NEURAL NETWORKS - DIFFERENTIATION OF CLASSIFICATION ENTROPY [J].

HARRINGTON, PD .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1993, 19 (02) :143-154

[9] CHEMOMETRIC DATA-ANALYSIS USING ARTIFICIAL NEURAL NETWORKS [J].

LIU, Y ;

UPADHYAYA, BR ;

NAGHEDOLFEIZI, M .

APPLIED SPECTROSCOPY, 1993, 47 (01) :12-23

[10] EVALUATION OF NEURAL NETWORKS BASED ON RADIAL BASIS FUNCTIONS AND THEIR APPLICATION TO THE PREDICTION OF BOILING POINTS FROM STRUCTURAL PARAMETERS [J].

LOHNINGER, H .

JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 1993, 33 (05) :736-744

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