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 equipped 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**

**Example text**

Ny ,nu . . kny (k)sj (10) 36 M. Lawry´ nczuk for j = 1, . . , N . Step-response coeﬃcients of the linear part of the model are denoted by sm,n for all j = 1, . . , N , n = 1, . . , nu , m = 1, . . , ny [11]. j Using (9), the output prediction vector can be expressed as a sum of a forced trajectory which depends only on the future (on future control moves u(k)) and a free trajectory y 0 (k), which depends only on the past where ˆ (k) = G(k) u(k) + y 0 (k) y (11) ⎤ ⎡ 0 ⎤ yˆ(k + 1|k) y (k + 1|k) ⎢ ⎥ ⎢ ⎥ ..

Hu, Q. ) RSCTC 2010. LNCS (LNAI), vol. 6086, pp. 669–677. Springer, Heidelberg (2010) 8. : Neural networks for modelling and control of dynamic systems. Springer, London (2000) 9. : Model predictive control based on Wiener models. Chemical Engineering Science 53, 75–84 (1998) 10. : A survey of industrial model predictive control technology. Control Engineering Practice 11, 733–764 (2003) 11. : Advanced control of industrial processes, Structures and algorithms. pl 2 Abstract. It is well known fact that organizing different predictors in an ensemble increases the accuracy of prediction of the time series.

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 following MPC algorithms are compared: a) the classical MPC algorithm based on the linear model, b) the discussed MPC-NPAL algorithm based on the neural Wiener model and quadratic programming, c) the MPC-NO algorithm with on-line nonlinear optimisation, it uses the same neural Wiener model.