This paper presents a new neural network system called the Evolving Tree. This network resembles the Self-Organizing map, but deviates from it in several aspects, which are desirable in many analysis tasks. First of all the Evolving Tree grows automatically, so the user does not have to decide the network’s size before training. Secondly the network has a hierarchical structure, which makes net...

Background modeling is often used in the context of moving objects detection from static cameras. Numerous methods have been developed over the recent years and the most used are the statistical ones. This paper describes the current state-of-art in background modeling methods for moving object detection. We also propose a method for background modeling based on texture features and self organi...

this paper investigates the forecasting gold coin futures contract price in iran mercantile exchange. this research has presented a hybrid model based on genetic fuzzy systems (gfs) and artificial neural network (ann) to forecast the gold futures contract, at first, we use stepwise regression analysis (sra) to determine factors which have most influence on stock prices. at the next stage we div...

We present a neural network methodology for clustering large data sets into pertinent groups. We applied this methodology to analyze the phytoplankton absorption spectra data gathered by the Laboratoire d'Océanographie de Villefranche. We first partitioned the data into 100 classes by means of a self-organizing map (SOM) and then we clustered these classes into 6 significant groups. We focused ...

Text collections may be regarded as an almost perfect application arena for unsupervised neural networks. This because many operations computers have to perform on text documents are classiication tasks based on noisy patterns. In particular we rely on self-organizing maps which produce a map of the document space after their training process. From geography, however, it is known that maps are ...

This paper presents the application of three di1erent types of neural networks to the 2-D pattern recognition on the basis of its shape. They include the multilayer perceptron (MLP), Kohonen self-organizing network and hybrid structure composed of the self-organizing layer and the MLP subnetwork connected in cascade. The recognition is based on the features extracted from the Fourier and wavele...

In this paper ensembles of self organizing NNs through fusion are introduced. In these ensembles not the output signals of the base learners are combined, but their architectures are properly merged. Merging algorithms for fusion and boosting-fusion-based ensembles of SOMs, GSOMs and NG networks are presented and positively evaluated on benchmarks from the UCI database.

In this research, It is first time that a supervised Self-Organizing Map (SOM) neural network is introduced as a classifier for Arabic handwriting. Classification has been achieved in two different strategies, in first strategy, we use one classifier for all 53 Arabic Character Basic Shapes CBSs in training and testing phases, in second strategy we use three classifiers and three subsets of 53 ...

Within forest growth modeling it is customary to employ LOGIT models to predict individual tree mortality. In this paper we use Learning Vector Quantization and the self-organizing map as diierent formalisms to predict individual tree mortality. The data set for this study came from permanent sample plots in uneven-aged Norway spruce (Picea abies L. Karst) stands in Austria. After parameterizin...

Artificial neural networks can be used effectively to identify and classify multiple events in a seismic data set. We use a specialized neural network, a self-organizing map, that tries to establish rules for the characterisation of the physical problem. Selected seismic data attributes are used as input patterns, such that the self-organizing map arranges the data in a manner that forms cluste...