By Ivan Bratko (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)
The two-volume set LNCS 6593 and 6594 constitutes the refereed lawsuits of the tenth foreign convention on Adaptive and traditional Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers provided have been conscientiously reviewed and chosen from a complete of one hundred forty four submissions. the 1st quantity comprises forty two papers and a plenary lecture and is prepared in topical sections on neural networks and evolutionary computation.
Read Online 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 info for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part I
Simulation results: the MPC-NO algorithm with nonlinear optimisation based on the neural Wiener model (solid line) and the MPC-NPAL algorithm with quadratic programming based on the same model (dashed line) 40 6 M. Lawry´ nczuk Conclusions In contrast to MPC algorithms in which an exact linearisation of the nonlinear model is used for prediction [4,5], the described MPC-NPAL algorithm is based on approximate linearisation. Nevertheless, control quality is quite good, similar to that obtained in the MPC-NO approach with nonlinear on-line optimisation.
2 Principal Component Analysis In this solution the weighted voting of the individual predictors is substituted by the linear transformation of the data provided by PCA. The PCA represents a classical statistical technique for analyzing the covariance structure of the multivariate statistical observations, enhancing the most important elements of information ,,. , w K ]T is the transformation matrix formed by K eigenvectors of the covariance matrix Rxx associated with K largest eigenvalues.
C Springer-Verlag Berlin Heidelberg 2011 Evaluating Reliability of Single Classiﬁcations of Neural Networks 23 method from . They were evaluated with a simple neural network on twenty domains gathered from the UCI Machine Learning Repository . 1 Related Work The most relevant related work is in the area of reliability estimation for individual examples. An appropriate criterion for diﬀerentiating between various approaches is whether they target a speciﬁc predictive model or they are modelindependent.