By Ivan Bratko (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)
The two-volume set LNCS 6593 and 6594 constitutes the refereed complaints of the tenth foreign convention on Adaptive and typical 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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Extra resources for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part I
We consider four additional estimators of single prediction reliability in the following. When measuring the distance between two probability distributions, the Hellinger distance was used. 1 Local Modeling of Prediction Error Let K be the predictor’s class probability distribution for a given unlabeled example (x, ). This approach to local estimation of prediction reliability is based on the nearest neighbors’ labels. Given a set of nearest neighbors N = [(x1 , C1 ), . . , (xk , Ck )], where Ci is the true label of the i-th nearest neighbor, the estimate CNK (CN eighbors − K) is for the unlabeled example deﬁned as the average distance between the prediction based on k nearest neighbors and the example’s prediction K: k i=1 (Ci , K) (3) k CNK is obviously not a suitable reliability estimate for the k-nearest neighbors algorithm, as they both work by the same principle.
In: R Foundation for Statistical Computing, Vienna (2006) Nonlinear Predictive Control Based on Multivariable Neural Wiener Models Maciej Lawry´ nczuk Institute of Control and Computation Engineering, Warsaw University of Technology ul. pl Abstract. This paper describes a nonlinear Model Predictive Control (MPC) scheme in which a neural Wiener model of a multivariable process is used. The model consists of a linear dynamic part in series with a steady-state nonlinear part represented by neural networks.
Taking into account the discrete-time diﬀerence equation (3) which deﬁnes the linear part of the model, one obtains a linear approximation of the whole nonlinear Wiener model for the current operating point (8) A(q −1 )y(k) = B(k, q −1 )u(k) where B(k, q −1 ) = K(k)B(q −1 ). Predictions calculated from the approximate model (8) can be compactly expressed as functions of future control increments (the inﬂuence of the past is not shown) yˆ(k + 1|k) =S 1 (k) u(k|k) + . . yˆ(k + 2|k) =S 2 (k) u(k|k) + S 1 (k) u(k + 1|k) + .