Download Artificial Intelligence and Soft Computing - ICAISC 2004: by Danilo Ardagna, Chiara Francalanci, Vincenzo Piuri, Fabio PDF

By Danilo Ardagna, Chiara Francalanci, Vincenzo Piuri, Fabio Scotti (auth.), Leszek Rutkowski, Jörg H. Siekmann, Ryszard Tadeusiewicz, Lotfi A. Zadeh (eds.)

This e-book constitutes the refereed lawsuits of the seventh foreign convention on man made Intelligence and smooth Computing, ICAISC 2004, held in Zakopane, Poland in June 2004.

The 172 revised contributed papers offered including 17 invited papers have been rigorously reviewed and chosen from 250 submissions. The papers are equipped in topical sections on neural networks, fuzzy platforms, evolutionary algorithms, tough units, tender computing in type, photograph processing, robotics, multiagent structures, difficulties in AI, clever keep watch over, modeling and procedure identity, clinical purposes, mechanical functions, and functions in a variety of fields.

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Extra resources for Artificial Intelligence and Soft Computing - ICAISC 2004: 7th International Conference, Zakopane, Poland, June 7-11, 2004. Proceedings

Example text

By including this term, we avoid the trivial solution yi = 0, ∀i. The second terms force the output signals to be as far as possible from Gaussianity, since the higher order cumulants are a natural measure of non-Gaussianity and they will vanish for Gaussian signals. It can be shown that by minimizing the cost function using the natural gradient approach, we can derive the following equivariant iterative algorithm ΔW(l) = W(l + 1) − W(l) = ηl [I − C1,q (y, y) Sq+1 (y)] W(l), where Sq+1 (y) = sign(diag(C1,q (y, y))).

The problems and methods described in the paper for static (memoryless) systems have been extended to systems described by a dynamical knowledge representation. The formalism of the uncertain variables and its application in a wide spectrum of uncertain systems (complexes) of operations, systems with distributed knowledge, pattern recognition and various practical systems) has been used as a basis for a uniform description of analysis 28 Z. Bubnicki and decision making in uncertain systems, presented in the book [12], where one can find the indications of new problems and perspectives in this area.

34 3 A. M. Zurada Sparse Component Analysis and Sparse Signal Representations Sparse Component Analysis (SCA) and sparse signals representations (SSR) arise in many scientific problems, especially, where we wish to represent signals of interest by using a small (or sparse) number of basis signals from a much larger set of signals, often called dictionary. Such problems arise also in many applications such as electro-magnetic and biomagnetic inverse problems (EEG/MEG), feature extraction, filtering, wavelet denoising, time-frequency representation, neural and speech coding, spectral estimation, direction of arrival estimation, failure diagnosis and speed-up processing [1,13].

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