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 normal 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 contains forty two papers and a plenary lecture and is equipped in topical sections on neural networks and evolutionary computation.
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
B1,n b1 q + . . + bnB q nB q 1 ⎥ ⎢ .. .. B(q −1 ) = ⎣ ⎦ . . n ,1 n ,1 n ,nu −1 b1 y q −1 + . . + bnyB q −nB . . b1 y q n ,nu −nB + . . + bnyB q The backward shift operator is denoted by q −1 , integers nA , nB , τ deﬁne the order of dynamics, τ ≤ nB . Outputs of the dynamic part are ⎡ nu nB ⎤ nA ⎤ ⎢ ⎢ x1 (k) ⎢ ⎢ .. ⎥ ⎢ x(k) = ⎣ . ⎦ = ⎢ ⎢ ⎢ xny (k) ⎣ ⎡ b1,r l ur (k − l) − a1l x1 (k − l) ⎥ ⎥ r=1 l=1 l=1 ⎥ ⎥ .. ⎥ . ⎥ nu nB nA ⎥ ny ,r ny bl ur (k − l) − al xny (k − l) ⎦ r=1 l=1 (4) l=1 The nonlinear steady-state part of the model is described by the equation y(k) = g(x(k)) where the function g : Rny → Rny is represented by ny MultiLayer Perceptron (MLP) feedforward neural networks with one hidden layer .
E. using the prediction equation (11), the MPC optimisation problem (2) becomes an easy to solve quadratic programming task min u(k) y ref (k) − G(k) u(k) − y 0 (k) 2 + u(k) 2 Λ subject to umin ≤ J u(k) + u(k − 1) ≤ umax (14) − umax ≤ u(k) ≤ umax y min ≤ G(k) u(k) + y 0 (k) ≤ y max Deﬁnitions of all vectors and matrices are given in . The discussed MPC algorithm is named MPC with Nonlinear Prediction and Approximate Linearisation (MPC-NPAL) in contrast to the MPC-NPL algorithm with an inverse steady-state model and exact linearisation .
A family of model predictive control algorithms with artiﬁcial neural networks. International Journal of Applied Mathematics and Computer Science 17, 217–232 (2007) 6. : Predictive control with constraints. Prentice Hall, Harlow (2002) 7. : Application of fuzzy Wiener models in eﬃcient MPC algorithms. , 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.