Titlebook: Artificial Intelligence and Soft Computing – ICAISC 2008; 9th International Co Leszek Rutkowski,Ryszard Tadeusiewicz,Jacek M. Zur Conferenc

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Input Signals Normalization in Kohonen Neural Networksons, is proposed. The Kohonen neural networks are considered as classifying systems. The main topic of this paper is proposal of applying stereographic projection as an input signals normalization procedure. Both theoretical justification is discussed and results of experiments are presented. It tur

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thrombosis

The Influence of Training Data Availability Time on Effectiveness of ANN Adaptation Processve data in time still tuning themselves. In opposite to them ANNs usually work on the training data (TD) acquired in the past and are totally available at the beginning of the adaptation process. Because of this the adaptation methods of the ANNs can be sometimes more effective than the natural trai

诗集

WWW-Newsgroup-Document Clustering by Means of Dynamic Self-organizing Neural Networksional WWW-newsgroup-document clustering problem. The collection of 19 997 documents (e-mail messages of different . newsgroups) available at WWW server of the School of Computer Science, Carnegie Mellon University (www.cs.cmu.edu/ TextLearning/datasets.html) has been the subject of clustering. A bro

In-Situ

Municipal Creditworthiness Modelling by Kohonen’s Self-organizing Feature Maps and LVQ Neural Networorks for municipal creditworthiness classification. The model is composed of Kohonen’s Self-organizing Feature Maps (unsupervised learning) whose outputs represent the input of the Learning Vector Quantization neural networks (supervised learning).

DEVIL

Fast and Robust Way of Learning the Fourier Series Neural Networks on the Basis of Multidimensional nted. The method proposed represents high speed of operation and outlier robustness. It allows easy reduction of network structure following its training process. The paper presents also the ways of applying the method to modelling of dynamic controlled systems. It is very easy to prepare a program

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