Guided by the principles of geometric independent component analysis (ICA), we present a new approach (SOMICA) to linear geometric ICA using a self-organizing map (SOM). We observe a considerable improvement in separation quality of different distributions, albeit at high computational costs. The SOMICA algorithm is therefore primarily interesting from a theoretical point of view bringing toget...

The Artificial Neural Networks method is applied on visual working efficiency of cockpit. A Self-Organizing Map (SOM) network is demonstrated selecting material with near properties. Then a Back-Propagation (BP) network automatically learns the relationship between input and output. After a set of training, the BP network is able to estimate material characteristics using knowledge and criteria...

In this paper the basic principles and developments of an unsu-pervised learning algorithm, the Self-Organizing Map (SOM) and a supervised learning algorithm, the Learning Vector Quantization (LVQ) are explained. Some practical applications of the algorithms in data analysis, data visual-ization and pattern recognition tasks are mentioned. In the end of the paper new results are reported about ...

Abbreviations ChIP Chromatin immunoprecipitation EST Expressed sequence tag ORF Open reading frame PCA Principle component analysis SAGE Serial analysis of gene expression SOM Self-organizing map SVM Support vector machine

The bandwidth reduction or storage lowering in digital image transmission confers to the image compression a key role. In this paper, we propose a new approach for lossy image compression: the source image is vector quantized by applying Self-Organizing Map (SOM) with several dictionaries. Each dictionary is originally designed based on the feature vectors resulted after applying the Walsh-Hada...

The Self-Organizing Map (SOM) is a powerful neural network method for the analysis and visualisation of high-dimensional data. In this paper, the SOM algorithm is applied to the analysis of the technology of world paper and pulp industry. It is seen that the method can be used on environmental, technological and nancial data to produce a comprehensive view of the industry as a whole.

Texture analysis has a wide range of real-world applications. This paper presents a novel technique for texture feature extraction and compares its performance with a number of other existing techniques using a benchmark image database. The proposed feature extraction technique uses 2 D D R transform and self-organizing map (SOM). A combination of 2D-DFT and SOM with optimal parameter settings ...

Clustering of data is one of the main applications of the Self-Organizing Map (SOM). U-matrix is a commonly used technique to cluster the SOM visually. However, in order to be really useful, clustering needs to be an automated process. There are several techniques which can be used to cluster the SOM autonomously, but the results they provide do not follow the results of U-matrix very well. In ...

Abstract — This study presents a new concept that generalizes the self-organizing map (SOM) by adopting the idea of modular network, which we call “modular network SOM (mnSOM)”. In the mnSOM, each codebook vector in the conventional SOM is replaced by a functional module which is a neural network. With mnSOM, the application targets can be widely expanded from fields involving vectorized data t...

This paper reports application of neurofuzzy inference system (NFIS) and self organizing feature map neural networks (SOM) on detection of contact state in a block system. In this manner, on a simple system, the evolution of contact states, by parallelization of DDA, has been investigated. So, a comparison between NFIS and SOM results has been presented. The results show applicability of the pr...