Download Artificial Intelligence and Soft Computing – ICAISC 2008: by Andrzej Bielecki, Marzena Bielecka, Anna Chmielowiec PDF

By Andrzej Bielecki, Marzena Bielecka, Anna Chmielowiec (auth.), Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)

This publication constitutes the refereed complaints of the ninth foreign convention on synthetic Intelligence and smooth Computing, ICAISC 2008, held in Zakopane, Poland, in June 2008.

The 116 revised contributed papers provided have been conscientiously reviewed and chosen from 320 submissions. The papers are geared up in topical sections on neural networks and their functions, fuzzy platforms and their purposes, evolutionary algorithms and their functions, class, rule discovery and clustering, picture research, speech and robotics, bioinformatics and scientific functions, a variety of difficulties of synthetic intelligence, and agent systems.

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Additional resources for Artificial Intelligence and Soft Computing – ICAISC 2008: 9th International Conference Zakopane, Poland, June 22-26, 2008 Proceedings

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Xn )T , w j = (wj1 , wj2 , . . , wjn )T ), yj = w Tj x . The learning data consists of L input vectors x l (l = 1, 2, . . , L). The first stage of any WinnerTakes-Most (WTM) learning algorithm that can be applied to the considered network, consists in determining the neuron jx winning in the competition of neurons when learning vector x l is presented to the network. ,m d(x l , w j ), (1) where d(x l , w j ) is a distance measure between x l and w j ; throughout this paper, a distance measure d based on the cosine similarity function S (most often used for determining similarity of text documents [1]) will be applied: d(x l , w j ) = 1 − S(x l , w j ) = 1 − x Tl w j =1− xl wj n i=1 (xli wji ) , n n 2 2 x w i=1 li i=1 ji (2) ( .

Pl/~drapala/ Abstract. Novel convergence properties of identification algorithm for complex input-output systems, which uses recurrent neural networks, are derived. By the term ”complex system” we understand a system containing interconnected sub processes (elementary processes), which can operate separately. Each element of the complex system is modeled by a multi-input, multi-output neural network. A model of the whole system is obtained by composing all neural networks into one global network.

P. pl Abstract. The paper presents a clustering technique based on dynamic self-organizing neural networks and its application to a large-scale and highly multidimensional WWW-newsgroup-document clustering problem. html) has been the subject of clustering. A broad comparative analysis with nine alternative clustering techniques has also been carried out demonstrating the superiority of the proposed approach in the considered problem. 1 Introduction The rapidly increasing volume of electronically available World-Wide-Web resources makes more and more important the issue of helping users to locate and access relevant information as well as to organize it in an intelligible way.

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