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

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 overseas 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 rigorously reviewed and chosen from 320 submissions. The papers are geared up 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 clinical functions, numerous difficulties of synthetic intelligence, and agent systems.

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2, pp. 671–674 (1996) 11. , Beale, R. ): Handbook of Neural Computation. IOP Publishing Ltd. and Oxford University Press, Bristol and New York (1997) 12. : A New Extension of Self-optimizing Neural Networks for Topology Optimization. , Zadro˙zny, S. ) ICANN 2005. LNCS, vol. 3696, pp. 415–420. Springer, Heidelberg (2005) 13. : Comparison of Plasticity of Self-Optimizing Neural Networks and Natural Neural Networks. R. ) Proc. of ICANN 2005, pp. 156–165. Springer, Heidelberg (2005) 14. : Self-Optimizing Neural Networks.

As stated before, learning algorithm (6) finds suboptimal (from a point of view of performance index (5)) solution only when values of coefficient η are selected properly at each time step n. Otherwise it diverges or oscillates ([8], [9], [10]). We use in learning procedure many correction coefficients, different for each single neural network and for each layer, [3]. Let η (m) (n), η (m) (n), η (m) (n) denote m-th neural network’s learning coefficients for output linear unit, hidden nonlinear neurons and feedback connections, respectively.

Grzech, A. ) Knowledge Engineering and Experts Systems, OWPN, Wroclaw, pp. 194–206 (2003) 8. : Neural network indicating maxima and ridges in two-dimensional signal. , Kallio, S. ) Engineering Applications of ANN, Proc of Int. Conf. EANN 1995, Helsinki, pp. 485–488 (1995) 9. : Adjusting parameters of twodimensional neural network. In: Proc. of the Second Conf. Neural Networks and Their Applications, Szczyrk, vol. 1 (1996) 10. : Application of neural networks to fringe samples analysis. , Kallio, S.

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