‘Integrated’ classification refers to the conjunctive or competitive use of two or more (neural) classifiers. A cooperative neural network system comprising two independently trained Kohonen networks and co-operating with the help of a Hebbian network, is described. The effectiveness of such a network is demonstrated by using it to retrieve images and related texts from a multi-media database. ...

The Kohonen self-organizing map (SOM) is an unsupervised neural network with a competitive learning strategy that uses a neighborhood lateral interaction function to discover the hidden topological structure of the input data and has both visualization and clustering properties. In this presentation, we propose batch SOM algorithms with automatic weighting of the variables to training the Kohon...

Feedforward unsupervised models cover a wide range of neural networks with various applications. In this section, we discuss three widely used models. (i) Kohonen’s selforganizing map, also called the Kohonen network , the self-organizing feature map, or the topological map, is intended to map a high-dimensional space into a oneor twodimensional space, preserving the topology of the input space...

Geological facies interpretation is essential for reservoir studying. The method of classification and identification seismic traces is a powerful approach for geological facies classification and distinction. Use of neural networks as classifiers is increasing in different sciences like seismic. They are computer efficient and ideal for patterns identification. They can simply learn new algori...

Background. A Kohonen topological map is an artificial intelligence system of the connectionist school (neural networks). The map learns the typical features of the subclasses in the learning set by means of a shortest Euclidean distance algorithm, after which selfadaptation of the neurons occurs. By its ability of selforganization and generalization, a Kohonen map is useful for pattern recogni...

Kohonen network was applied in the analysis of fluent and non-fluent utterances of any length. The input vector consisted of sound level values measured in dB(A) in 1/3-octave bands. Fragments of stuttering people’s and fluent speakers’ utterances were analysed. The optimum neural network was chosen of a 5×5 rectangle topology. Time arrangement of winner neurons in the network extracts single d...

This paper describes the application of two different neural network types for stock price prediction. The prediction is carried out by Kohonen self-organizing maps and error backpropagation algorithm. Both experimental networks deal with price change intervals in contradiction to precise value prediction. The results are presented and its comparative analysis is performed in this paper, as wel...