The Kohonen and CPANN toolbox is a collection of MATLAB modules for developing Kohonen Maps and Counterpropagation Artificial Neural networs (CPANNs), Supervised Kohonen networks and XY-fused networks. These are well known neural networks aimed to cluster analysis and the study of data structure (Kohonen Maps) and to the data classification (CPANNs, Supervised Kohonen networks and XY-fused networks). A graphical user interface (GUI), which allows an easy model calculation and analysis of results, is also provided with the toolbox. Help files: HTML files are provided toghter with the MATLAB files in order to help the user. The HTML help provides some underlying information on Kohonen Maps and CPANNs (see Theory section); it also explains how to prepare your data, how to handle the Neural Network settings and how to calculate the models. An example of analysis (on the classical IRIS data set) is shown. The toolbox is freeware and may be used if proper reference is given to the authors. Preferably refer to the followign papers: Ballabio D, Consonni V, Todeschini R. (2009) The Kohonen and CP-ANN toolbox: a collection of MATLAB modules for Self Organizing Maps and Counterpropagation Artificial Neural Networks. Chemometrics and Intelligent Laboratory Systems, 98, 115-122 Ballabio D, Vasighi M. (2012) A MATLAB Toolbox for Self Organizing Maps and supervised neural network learning strategies. Chemometrics and Intelligent Laboratory Systems, 118, 24-32 The Kohonen and CPANN toolbox for MATLAB is distributed with an Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence

{"references": ["Ballabio D, Consonni V, Todeschini R. (2009) The Kohonen and CP-ANN toolbox: a collection of MATLAB modules for Self Organizing Maps and Counterpropagation Artificial Neural Networks. Chemometrics and Intelligent Laboratory Systems, 98, 115-122", "Ballabio D, Vasighi M. (2012) A MATLAB Toolbox for Self Organizing Maps and supervised neural network learning strategies. Chemometrics and Intelligent Laboratory Systems, 118, 24-32"]}