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 e-book constitutes the refereed lawsuits of the ninth foreign convention on man made Intelligence and delicate Computing, ICAISC 2008, held in Zakopane, Poland, in June 2008.

The 116 revised contributed papers awarded have been rigorously reviewed and chosen from 320 submissions. The papers are prepared in topical sections on neural networks and their purposes, fuzzy structures and their functions, evolutionary algorithms and their functions, type, rule discovery and clustering, photo research, speech and robotics, bioinformatics and scientific functions, quite a few difficulties of synthetic intelligence, and agent systems.

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

Sample text

The SONN topology optimization process is based on discrimination coefficients (1) computed for all training data samples as well as for some subgroups Table 1. Comparison of classification results 32 E. Dudek-Dyduch and A. Horzyk of them. In order to find the optimal SONN topology there is necessary to create only these connections which are necessary to classify correctly and unambiguously all training data using only the input features with maximal discrimination properties [13],[14]. SONN weights parameters and a SONN topology are computed simultaneously during construction process of the network [12],[14].

E. n. The intersections are parallel to the axis y. The networks of the subsystems are called X(i)-net and Y (i)-net respectively. Outputs of X-net and Y -net are connected in such a way that k-th output of X(i)-net is summed with i-th output of Y (k)-net. The presented architecture of the network is only one solution. Another two dimensional network based on hexagonal structure is given in [5]. One dimensional network. The structure of a one-dimensional network that indicates relative maxima is given if fig.

In: Proc. of IV Conf. Neural Networks and Their Applications, Zakopane, pp. 487–497 (1999) 6. : A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 270–280 (1989) Stable Learning Algorithm of Global Neural Network for Identification of Dynamic Complex Systems Jarosław Drapała, Jerzy Świątek, and Krzysztof Brzostowski Institute of Information Science and Engineering, Wrocław University of Technology, Wyb. pl/~drapala/ Abstract. Novel convergence properties of identification algorithm for complex input-output systems, which uses recurrent neural networks, are derived.

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