Kohonen neural networks applications

Y. Vander Heyden, P. Vankeerberghen, M. Novic, J. Zupan and D. L. Massart, The application of Kohonen neural networks to diagnose calibration problems in atomic absorption spectrometry, Talanta, 51(3), 2000, 455-466. 

Kireev, D.B., Chretien, J.R., Bernard, P. and Ros, F. (1998). Application of Kohonen Neural Networks in Classification of Biologically Active Compounds. SAR QSAR Environ.Res., 8, 93-107. 

A type of neural network that has been proved to be successful in a series of applications is based on self-organizing maps (SOMs) or Kohonen neural networks [61]. Whereas most of the networks are designed for supervised learning tasks (i.e., the relationship between input and output must be known in form of a mathematical model), Kohonen neural networks are designed primarily for unsupervised learning where no prior knowledge about this relationship is necessary [62,63]. 

Kirew DB, Chretien JR, Bernard R Ros F (1998) Application of Kohonen neural networks in classification of biologically active compounds. SAR QSAR Envssss Res 8 93-107... 

This format was developed in our group and is used fruitfully in SONNIA, software for producing Kohonen Self Organizing Maps (KSOM) and Coimter-Propaga-tion (CPG) neural networks for chemical application [6]. This file format is ASCII-based, contains the entire information about patterns and usually comes with the extension "dat . 

To understand neural networks, especially Kohonen, counter-propagation and back-propagation networks, and their applications... 

The usage of a neural network varies depending on the aim and especially on the network type. This tutorial covers two applications on the one hand the usage of a Kohonen network for classification, and on the other hand the prediction of object properties with a counter-propagation network,... 

R. Goodacre, J. Pygall and D.B. Kell, Plant seed classification using pyrolysis mass spectrometry with unsupervised learning the application of auto-associative and Kohonen artificial neural networks. Chemom. Intell. Lab. Syst., 33 (1996) 69-83. 

R. Lletf, L.A. Sarabia, M.C. Ortiz, R. Todeschini, M.P. Colombini, Application of the Kohonen Artifical Neural Network in the Identification of Proteinaceous Binders in Samples of Panel Painting using Gas Chromatography Mass Spectrometry, Analyst, 128, 281 286 (2003). 

Just as there are several varieties of evolutionary algorithm, so the neural network is available in several flavors. We shall consider feedforward networks and, briefly, Kohonen networks and growing cell structures, but Hop-field networks, which we shall not cover in this chapter, also find some application in science.31... 

Not all neural networks are the same their connections, elemental functions, training methods and applications may differ in significant ways. The types of elements in a network and the connections between them are referred to as the network architecture. Commonly used elements in artificial neural networks will be presented in Chapter 2. The multilayer perception, one of the most commonly used architectures, is described in Chapter 3. Other architectures, such as radial basis function networks and self organizing maps (SOM) or Kohonen architectures, will be described in Chapter 4. 

Rose, V.S., Croall, I.F. and MacFie, H. J. H. (1991) An application of unsupervised neural network methodology (Kohonen topology-preserving mapping) to QSAR analysis. Quant. Struct.-Act. Relat. 10 6-15. 

Clustering. In this case, neural networks are used to assign similar patterns to the same cluster. Often Kohonen networks are used. Typical applications can be found in the area of chemical analysis (Tokutaka et al, 1999) and weather pattern recognition (Ambroise et al, 2000). 

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