Software description
Counter-propagation neural networks in Matlab
Igor Kuzmanovski
a,b,
⁎
, Marjana Novič
b
a
Institut za hemija, PMF, Univerzitet “Sv. Kiril i Metodij”, P.O. Box 162, 1001 Skopje, Macedonia
b
National Institute of Chemistry, Ljubljana, Hajdrihova 19, SLO-1115 Ljubljana, Slovenia
Received 9 May 2007; received in revised form 16 July 2007; accepted 20 July 2007
Available online 2 August 2007
Abstract
The counter-propagation neural networks have been widely used by the chemometricians for more than fifteen years. This valuable tool for
data analysis has been applied for solving many different chemometric problems. In this paper the implementation of counter-propagation neural
networks in Matlab environment is described. The program presented here is an extension of Self-Organizing Maps Toolbox for Matlab that is not
widely used by chemometricians. This program coupled with the excellent visualization tools available in Self-Organizing Maps Toolbox and with
other valuable functions in this environment could be of great interest for analysis of chemical data. The use of the program is demonstrated on the
development of the regression and classification models.
© 2007 Elsevier B.V. All rights reserved.
Keywords: Counter-propagation neural networks; Kohonen self-organizing maps; Classification; Regression; Matlab
1. Introduction
Kohonen self-organizing maps (SOM) [1,2] and counter-
propagation neural networks (CPNN) [3–8] have become
common tools in the chemometric community in the last fifteen
to twenty years. Their principle is an experience-based mod-
eling, which can be exploited for black box models; as such, they
have shown good results in many different application in
chemistry where analytical function which connects input with
output variables does not exist or it is impossible to derive one
(as in QSAR studies) or where there are deviations from linear
behavior which is a case in their a pplications in analytical
chemistry.
Self-Organizing Maps Toolbox [9–11] for Matlab [12] is
available on internet for free [10]; nevertheless the authors have
not found many applications of this software in chemometrics
[13–16]. Due to its simplicity, comfort of use for their excellent
incorporation in visualization tools and easy integration with
other toolboxes available in Matlab environment, we decided to
develop a program for counter-propagation neural networks
(CPNN) based on this toolbox.
The algorithm for the counter-propagation neural network
tool was written and elaborated for the use in Matlab environ-
ment. It was integrated into the SOM Toolbox to exploit the
advantages of the standard visua lization tools available. The
performances of the program are demonstrated on the models
built for prediction of the boiling points for the series of
substances [17,18] and for classification of famous Italian olive
oils data set [19–21].
2. Counter-propagation neural networks algorithm
From the didactical point of view this type of artificial neural
networks are usually represented as consisting of two layers
(Fig. 1): one layer is the compe titive — Kohonen layer and the
other is the output layer. The input layer in CPNN performs the
mapping of the multidimensional input data into lower dimen-
sional array (most often two-dimensional, since the exploration
and visualization of more than two-dimensional SOMs are not
easy for human perception). The mapping is performed by use
of competitive learning — often called winner-takes-it-all
strategy. The main steps of the training procedure of CPNN are
presented on the flow chart given in Fig. 2.
A
vailable online at www.sciencedirect.com
Chemometrics and Intelligent Laboratory Systems 90 (2008) 84 – 91
www.elsevier.com/locate/chemolab
⁎
Corresponding author. Institut za hemija, PMF, Univerzitet “Sv. Kiril i
Metodij”, P.O. Box 162, 1001 Skopje, Macedonia. Tel.: +398 2 3249952; fax:
+398 2 3226865.
E-mail address: shigor@iunona.pmf.ukim.edu.mk (I. Kuzmanovski).
0169-7439/$ - see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.chemolab.2007.07.003
