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 booklet constitutes the refereed court cases of the ninth overseas convention on man made Intelligence and smooth Computing, ICAISC 2008, held in Zakopane, Poland, in June 2008.

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

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Stable Learning Algorithm of Global Neural Network 27 References 1. : Nonlinear Programming - Theory and Algorithms. Wiley-Interscience, A John Wiley & Sons Inc, Hoboken, New Jersey (2006) 2. : Identification of Control Plants. (in polish) PWN, Warsaw (1980) 3. : Diagonal Recurrent Neural Networks for Dynamic Systems Control. IEEE Transactions on Neural Networks 6(1), 144–155 (1995) 4. : Algorithm of Recurrent Multilayer Perceptrons Learning for Global Modeling of Complex Systems. In: Proc. of 16th International Conference on Systems Science ICSS 2007, Wrocław University of Technology, Wrocław, Poland, pp.

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]. Such strategy makes us possible to precisely assign to each feature representing any subgroup of TD the accurate and optimal weight value arising from its discrimination properties computed in a global view of all TD.

Owing to (7) and (15) for one output signal (βm in a single network case can be omitted), we have: Δw(m) (n) = −η(n) Taking (21) ∂y(m) (n) ∂e(m) (n) =− , we obtain: (m) ∂w ∂w(m) ∂y(m) (n) ΔV (n) = − ∂w(m) · ∂Q(n) ∂y (m) (n) (m) = ηe (n) . ∂w(m) ∂w(m) e (m) T η(n)e(m) (n) 1 ∂y (m) (n) (n) − 2 ∂w(m) ∂y(m) (n) =− ∂w(m) ∂y (m) (n) = ∂w(m) T η(n)e(m) (n) 2 η(n)e (m) (n) e 2 η(n) e (m) ∂y(m) (n) · ∂w(m) 2 (n) (m) ∂y (m) (n) ∂w(m) 1 ∂y (m) (n) (n) − 2 ∂w(m) 1 ∂y (m) (n) η(n) 2 ∂w(m) 2 η(n)e(m) (n) 2 −1 .

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