An artificial neural network was used to cluster proteins into families. The network, composed of 7 x 7 neurons, was trained with the Kohonen unsupervised learning algorithm using, as inputs, matrix patterns derived from the bipeptide composition of 447 proteins, belonging to 13 different families. As a result of the training, and without any a priori indication of the number or composition of ...

This work is devoted to describe a potential use of the 1-Dimensional Kohonen Networks in the automatic non-supervised segmentation and classification of computed tomography brain slices. Possible perspectives of application include the automatic delineation of areas on the cerebral map and the automatic correlation between new clinical cases with previous boarded and closed cases. The classifi...

We present a method based on the non-linear behaviour of neural network for the identification of the early-type population in the cores of galaxy clusters. A Kohonen Self Organising Map applied on a three-colour photometric catalogue of objects enabled us to select in each passband the elliptical galaxies. We measured in this way the luminosity function of the E/S0 galaxies selected in this wa...

This paper introduces the concept of dynamic embedding manifold (DEM), which allows the Kohonen self-organizing map (SOM) to learn dynamic, nonlinear input-ouput mappings. The combination of the DEM concept with the SOM results in a new modelling technique that we called Vector-Quantized Temporal Associative Memory (VQTAM). We use VQTAM to propose an unsupervised neural algorithm called Self-Or...

The current paper discusses the importance of the modern high resolution satellite imagery. The acquired high amount of data must be processed by an efficient way, where the used Kohonen-type self-organizing map has been proven as a suitable tool. The paper gives an introduction to this interesting method. The tests have shown that the multispectral image information can be taken after a resamp...