Download e-book for iPad: Advances in Computational Intelligence and Learning: Methods by Robert Babuška (auth.), Hans-Jürgen Zimmermann, Georgios

By Robert Babuška (auth.), Hans-Jürgen Zimmermann, Georgios Tselentis, Maarten van Someren, Georgios Dounias (eds.)

ISBN-10: 9401003246

ISBN-13: 9789401003247

ISBN-10: 9401038724

ISBN-13: 9789401038720

Advances in Computational Intelligence and studying: tools and Applications offers new advancements and functions within the sector of Computational Intelligence, which primarily describes equipment and ways that mimic biologically clever habit which will clear up difficulties which were tricky to unravel by way of classical arithmetic. ordinarily Fuzzy know-how, man made Neural Nets and Evolutionary Computing are thought of to be such approaches.

The Editors have assembled new contributions within the components of fuzzy units, neural units and computer studying, in addition to combos of them (so known as hybrid tools) within the first a part of the publication. the second one a part of the publication is devoted to functions within the components which are thought of to be so much correct to Computational Intelligence.

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Q Depending on the discipline, fmding a "good" estimate S: 91 p H 9l of the unknown function s using XY is called, to name a few,function approximation with data (numerical analysis); system identification (control engineering), or supervised learning (pattern recognition). Optimization of a model that produces an approximation S to S is usually guided by an index that measures the goodness of fit of S to XY. There are many such models, and paramount among them is the mean squared error (MSE) model, which seeks a function S from some specified set of functions S that solves the optimization problem (1) S may be a family of functions or a computational system, say 5 = {S(X; 9) : 9 E Q}, each member of which depends on a parameter vector 9 E Q .

In either case, once S is found, it is available for generalization (evaluation, recall) at points x' te X . , whether x' is inside or outside the convex hull ofX. q Ifwe have a way to determine output values Y' = {yi, ... ,y:n} c 91 for a set of test inputs X' ={Xi, ... , x:n} c 91 P with X'Y' Il XY = 0, then the number m L k=l \\y;, - SIX;' )11 2 is the test or generalization error associated with (and / determined by) the pair (S, X'Y ). We can always generate X', but since we don't know S, we cannot generate a corresponding Y / .

And M. , Fuzzy Systems: Modeling and Control, Kluwer, Norwell, MA. , Bezdek, J. , Pal, N. R. and Runkler, T. 2000. Some notes on fuzzy rule extraction by clustering, Proc. 5th Iberoamerican Symp. on Patt. , eds. F. Muge, R. C. Pinto and M. Piedade, ISBN 972-97711-1-1, 19-27. Runkler, T. A. and J. C. Bezdek, 1999. Alternating cluster estimation: a new tool for clustering and function approximation, IEEE Trans. Fuzzy Systems, 7(4), 377-393. Smith, S. P. and Jain, A. , 1984. Testing for uniformity in multidimensional data, IEEE Trans.

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Advances in Computational Intelligence and Learning: Methods and Applications by Robert Babuška (auth.), Hans-Jürgen Zimmermann, Georgios Tselentis, Maarten van Someren, Georgios Dounias (eds.)

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