Download Artificial Intelligence and Dynamic Systems for Geophysical by Professor Dr. Alexei Gvishiani, Professor Dr. Jacques Octave PDF

By Professor Dr. Alexei Gvishiani, Professor Dr. Jacques Octave Dubois (auth.)

ISBN-10: 3642077579

ISBN-13: 9783642077579

ISBN-10: 3662049333

ISBN-13: 9783662049334

The booklet provides new clustering schemes, dynamical structures and development acceptance algorithms in geophysical, geodynamical and usual risk functions. the unique mathematical process relies on either classical and fuzzy units versions. Geophysical and typical probability functions are quite often unique. notwithstanding, the factitious intelligence approach defined within the ebook should be utilized a long way past the boundaries of Earth technological know-how purposes. The e-book is meant for learn scientists, tutors, graduate scholars, scientists in geophysics and engineers

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T he solution of t his problem will allow to construct corr esponding expert system, that will serve as areal fuzzy realization of Syntactic Pattern Recognition Approach (Fuzzy S PARS). 3 Clustering Algorithms This seet ion descr ibes new constructions of elustering algorithms int ro duced in 2000-2001 by A. Gvishiani and S. 1 "Lig ht ing" and clustering in finite metric spaces Imagine that at every point of a finite metric space X = (x, d), there is a lamp emitting light . Let us see how a point is illu minat ed by t he other points .

The most natural way to aggregate all fuzzy structures (FSP)st(Y;) is by their fuzzy conjunction over E. However, other types of such a disjunction "collection" can be considered. A vector approach to the "collection" gives the following vector order on E: e I-----t {((FSP)st(yn)(e)}; s = 1, " ' , N; t = 1, " ', M . 46) we obtain solutions to the problem under consideration . 5 Dynamic Clsss iiicetiou A pproach Fuzzy version of SPARS algorithm (F SPARS) At first we introduce basic nations , being oriented towards seismological problems.

Let us ass urne t hat to t he mome nt T t he station F has received t he signals SI , • . , Sn wit h epicenters in t he point s X l, •.. , of the region X. The mapping {Xi 1-+ Si I ~} is act ua lly the result of the jo b produce d by the station F . We denote I = {x 1-+ Si I ~} and call I the main information kern al. It is convenient to consider I as a linear relat ion FI between X and S- project ions of FI :XI = {Xi I~}' SI = {Si In. T hus , I = (XI, FI, SI ). Representi ng t he signal sE S as a veetor of its components seismF(s)seismogram recorded at the station F , mF(s)-magnitude, ß F(s )-epi cent ral distance, hF(s)-depth , tF( s)-time in the sour ce, we obtain t he appearence of I as a funetion of a given database.

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Artificial Intelligence and Dynamic Systems for Geophysical Applications by Professor Dr. Alexei Gvishiani, Professor Dr. Jacques Octave Dubois (auth.)


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