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 foreign convention on Adaptive and usual Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers awarded have been rigorously 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.
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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
LNCS (LNAI), vol. 2430, pp. 1–8. Springer, Heidelberg (2002) 5. : Quality assessment of individual classiﬁcations in machine learning and data mining. Knowledge and Information Systems 9(3), 364–384 (2006) 6. : Pattern Recognition and Neural Networks, Cambridge (1996) 7. R Development Core Team: A Language and Environment for Statistical Computing. 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.
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Classical MPC algorithms use linear models. g. instability. Current research and practical applications concentrate on nonlinear MPC algorithms which use nonlinear models . In particular, MPC algorithms based on neural models attract attention [5,8,11]. Neural model are worth considering not only because they oﬀer good accuracy, but also because they have a reasonably small number of parameters and a simple structure. As a result, neural models can be eﬃciently used on-line in MPC. ˇ A. Dobnikar, U.