Download Artificial Intelligence and Soft Computing -- Icaisc 2004 by Rutkowski L. (Ed), Zadeh L. A. (Ed) PDF

By Rutkowski L. (Ed), Zadeh L. A. (Ed)

This publication constitutes the refereed court cases of the seventh overseas convention on man made Intelligence and gentle Computing, ICAISC 2004, held in Zakopane, Poland in June 2004.The 172 revised contributed papers provided including 17 invited papers have been conscientiously reviewed and chosen from 250 submissions. The papers are equipped in topical sections on neural networks, fuzzy structures, evolutionary algorithms, tough units, smooth computing in type, photograph processing, robotics, multiagent platforms, difficulties in AI, clever regulate, modeling and method identity, scientific functions, mechanical purposes, and purposes in a variety of fields.

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Extra resources for Artificial Intelligence and Soft Computing -- Icaisc 2004

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ICA seeks to ensure maximum independence, typically by minimizing or maximizing the higher order moments or cumulants of the outputs. For example, for signals corrupted by additive Gaussian noise, as measure of statistical independence, we can use higher order matrix cumulants in the for of the following objective function [1]: where denotes the cumulants of the signal and denotes the cross-cumulant matrix whose elements are The first term in the objective function assures that the determinant of the global separating matrix will not approach zero.

Note anyway that, over 2000 users, the genetic optimization found a minimum cost system by partitioning users in two server farms (last row of the first group). This contradicts the #3 rule of professional guidelines. In the power user group, due to the fixed application on the client side (Matlab) and to its computational-intensive nature, the best systems are composed by fat clients. The genetic optimization proves that it does not exist an intermediate “hybrid” solution that is less expensive.

These approaches lead to interesting and promising new ways of investigating and analyzing brain data and develop new hypotheses how the neural assemblies communicate and process information. This is actually an extensive and potentially promising research area. However, these approaches still remain to be validated at least experimentally.

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