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

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

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

The 172 revised contributed papers offered 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, gentle computing in type, photograph processing, robotics, multiagent platforms, difficulties in AI, clever keep watch over, modeling and method identity, clinical purposes, mechanical purposes, and functions in quite a few fields.

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Example text

E. in the case of complex numbers (G1,0,0 ) or quaternions (G0,2,0 ) we can see that the circle or the sphere are divide by means spherical vectors. g. + < αj >2n < y j >2n I, 13 (17) simply one consider as coefficients of the multivector basis the multiplications between the coefficients of blades of same degree. For clarity we introduce this operation “◦”which takes place implicitly in previous equation (5). Note that the cases of 2-state and 4-state (Complex numbers) can be solved by the multi-class real valued SVM, however in case of higher representations like the 16-state using quaternions, it would be awkward to resort to the multi-class real valued SVMs.

In the second description of the uncertainty (used in the definition of the soft variable and sufficient for nonparametric problems), it is not necessary to define m(Dx ). The details concerning the definition of the soft variable and its application to nonparametric decision problems are presented in [12]. m(Dx ) is a It is worth noting that for the uncertain variables v(x∈Dx ) mc (Dx ) is possibility measure with a specific interpretation, and vc (x∈Dx ) neither a belief nor a plausibility measure.

We observe records of sensor signals x(t) = [x1 (t), . . , xm (t)]T from a MIMO (multiple-input/multiple-output) nonlinear dynamical system. The objective is to find an inverse system, termed a reconstruction system, neural network or an adaptive inverse system, if it exists and is stable, in order to estimate the primary source signals s(t) = [s1 (t), . . , sn (t)]T . This estimation is performed on the basis of only the output signals y(t) = [y1 (t), . . , yn (t)]T and sensor signals. Preferably, the inverse system should be adaptive in such a way that it has some tracking capability in non-stationary environments.

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