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 ebook constitutes the refereed lawsuits of the ninth overseas convention on synthetic Intelligence and gentle Computing, ICAISC 2008, held in Zakopane, Poland, in June 2008.

The 116 revised contributed papers provided 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 purposes, evolutionary algorithms and their functions, class, rule discovery and clustering, picture research, speech and robotics, bioinformatics and clinical functions, a number of difficulties of synthetic intelligence, and agent systems.

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

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34 E. Dudek-Dyduch and A. Horzyk Fig. 1. Structure of the two-dimensional network indicating maxima and ridges with the transfer functions of a neuron, e-input signal (a), h(e) output signal (b) Fig. 2. Structure of the network ⎧ ⎨ h(e) = ⎩ 0 k(e+pd ) pg −pd 0 f or e < −pd f or − pd ≤ e ≤ pd f or e > pd (9) where pd , pg , k are parameters of positive values that denote, respectively, threshold point, saturation point and value of saturation (it is assumed usually that k = 1). The formula is a little bit different from the one used in literature [1] but it is more convenient for our aim.

In the next sections we provide stability analysis using second Lyapunov method, [7]. 3 Stability of Learning Algorithm for Local Networks Let us assume that all elements of the complex system are stable plants, which (m) means that absolute values of all feedback weights wj shouldn’t be greater than 1. Further discussion will be also clear if we restrict to SISO systems (Lm = Sm = 1 for all m), see Fig. 3. All conclusions drawn in this paper can be strictly generalized to multivariate systems, but we want to keep complexity of notation on a reasonable level.

The NNNs can be concluded as the best developed to adapt to the many times changing TD that are acquired in a long period of L. Rutkowski et al. ): ICAISC 2008, LNAI 5097, pp. 28–39, 2008. c Springer-Verlag Berlin Heidelberg 2008 The Influence of Training Data Availability Time 29 time. In contrast, we look forward that the ANNs should be usually created and trained on a basis of a given TD acquired in the past. TD does usually not change during training process. The time-expensive training process necessary for adapting and tuning of NNNs can be replaced with other adaptation or construction algorithms that can compute a NN structure and its parameters more quickly.

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