By Andrzej Bielecki, Marzena Bielecka, Anna Chmielowiec (auth.), Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)
This ebook constitutes the refereed lawsuits of the ninth foreign convention on man made Intelligence and tender 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 geared up in topical sections on neural networks and their functions, fuzzy structures and their purposes, evolutionary algorithms and their purposes, type, rule discovery and clustering, photo research, speech and robotics, bioinformatics and clinical functions, numerous difficulties of synthetic intelligence, and agent systems.
Read or Download Artificial Intelligence and Soft Computing – ICAISC 2008: 9th International Conference Zakopane, Poland, June 22-26, 2008 Proceedings PDF
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Extra resources for Artificial Intelligence and Soft Computing – ICAISC 2008: 9th International Conference Zakopane, Poland, June 22-26, 2008 Proceedings
The SONN topology puts together the most important information discriminative properties of diﬀerent features constructing the uniform classiﬁcation model for the given TD. The SONN is built up after the most important, well-diﬀerentiating and discriminating features of all TD samples. The SONN generalization property reﬂects the most important and characteristic information of individual classes for any given TD set. 4 ANNs for Feature Extraction Another new approach to creating ANNs is described and analyzed in the papers  and .
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 . r−1 dj,j+1 , where di,i+1 is the distance between the Condition 2: di,i+1 > α1 j=1r neurons no.
Performance factor of parallel realisation of the RTRN algorithm achieves 2100 for 10 inputs and 10 neurons and it grows fast when the number of network inputs or neurons grows. We observed that the performance of the proposed solution is very satisfactory. References 1. : Parallel Realisation of QR Algorithm for Neural Networks Learning. A. ) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 158–165. Springer, Heidelberg (2004) 2. : A Field Guide to Dynamical Recurrent Neural Networks. IEEE Press, Los Alamitos (2001) 3.