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New PDF release: Artificial Intelligence and Soft Computing – ICAISC 2008:

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

ISBN-10: 3540695729

ISBN-13: 9783540695721

ISBN-10: 3540697314

ISBN-13: 9783540697312

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

The 116 revised contributed papers offered have been conscientiously reviewed and chosen from 320 submissions. The papers are equipped in topical sections on neural networks and their purposes, fuzzy platforms and their purposes, evolutionary algorithms and their functions, type, rule discovery and clustering, photograph research, speech and robotics, bioinformatics and scientific functions, numerous 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

Each of subsystems is built of parallel one-dimensional ANNs. e. n. The intersections are parallel to the axis x. 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].

I and neuron no. i + 1; i ∈ {1, 2, . . , r − 1}, where r is the number of neurons in the original neuron chain or a given sub-chain. Condition 1: wini < β1 , where wini is the number of wins of i-th neuron and β1 is experimentally selected parameter (usually, for complex multidimensional WWW-document clustering, β1 assumes the value around 50). This condition allows to remove single neuron whose activity (measured by the number of its wins) is below an assumed level represented by parameter β1 .

According to the fact, that βm ∈ [0, 1], point β lie inside appropriate M -dimensional unit cube. An example for two dimensions (two neural networks) is illustrated in 4. Choice of a certain point α implies proper value of learning coefficient η (m) (n). Important and very interesting observation is that it is not necessary for all αm to be positive, which means that learning algorithm operating on all neural 26 D. Drapała, J. Świątek, and K. Brzostowski Fig. 4. Two-dimensional example of selecting α networks can be stable, even if at the same time stability condition is not fulfilled from some of the networks ”point of view”.

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Artificial Intelligence and Soft Computing – ICAISC 2008: 9th International Conference Zakopane, Poland, June 22-26, 2008 Proceedings by Andrzej Bielecki, Marzena Bielecka, Anna Chmielowiec (auth.), Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)


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