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Download Adaptive and Natural Computing Algorithms: 10th by Ivan Bratko (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à PDF

By Ivan Bratko (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)

The two-volume set LNCS 6593 and 6594 constitutes the refereed court cases of the tenth overseas convention on Adaptive and common Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers offered have been conscientiously reviewed and chosen from a complete of a hundred and forty four submissions. the 1st quantity comprises forty two papers and a plenary lecture and is equipped in topical sections on neural networks and evolutionary computation.

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Read or Download Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part I PDF

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Extra resources for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part I

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2. It has two inputs: u1 – the feed flow rate, u2 – the cooling substance flow rate, and two outputs: y1 – the product concentration, y2 – the product temperature. As a simulated process the Wiener system is used. 136097q −2 38 M. Lawry´ nczuk Fig. 2. The reactor The nonlinear steady-state part of the system is described by functions shown in Fig. 3 (valves with saturation for which inverse functions do not exist). Fig. 3 also shows neural approximations of the steady-state part (two networks with K 1 = K 2 = 5 hidden nodes are used).

The most often used practice is to train different neural predictors and then accept one which guarantees the best results of prediction on the validation data set. However better solution is to use all trained networks combined in an ensemble and integrate their results into final prediction. The general scheme of ensemble of predictors is presented in Fig. 1. Fig. 1. The general structure of ensemble system for prediction The important condition for including the predictor into ensemble is independent operation from the other and also similar level of prediction error.

Then the set of argminima of the expected error m Ez over C(X) contains the set conv{fa+ | a > 0 }, where fa+ = i=1 cai Kγad u with i ca = Kγad [u]+ v. Proof. By Theorem 2(i), the argminimum of Ez over HKγa (X) has the form fa+= d m a + a a i=1 ciKγd ui and thus fa ∈ span GKγd (X). By Theorem 5 (i), span GKγd (X) is dense in (C(X), . sup ) and by Proposition 1, Ez is continuous on (C(X), . sup). Thus by Proposition 2, fa+ is an argminimum of Ez over C(X). As the set of argminima is convex, the statement holds.

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