By Cristina Teixeira, José Covas, Thomas Stützle, António Gaspar-Cunha (auth.), Xiao-Zhi Gao, António Gaspar-Cunha, Mario Köppen, Gerald Schaefer, Jun Wang (eds.)
The 14th onlineWorld convention on smooth Computing in business purposes offers a different chance for tender computing researchers and practitioners to put up top of the range papers and speak about learn concerns intimately with out incurring an incredible rate. The convention has validated itself as a very international occasion on the net. the standard of the convention has enhanced through the years. The WSC14 convention has lined new traits in smooth computing to state-of-the-art functions. The convention has additionally extra new gains akin to neighborhood instruments, syndication, and multimedia on-line presentations.
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Extra info for Soft Computing in Industrial Applications: Algorithms, Integration, and Success Stories
P(cC |ri)), so that fuzzy decision trees obtained by the FID algorithm can be represented by an extended fuzzy classifier proposed in . Therefore the proposed form of the classification rules is the following: ri : If x1 is Ai,1 (x1,k ) And . . xn is Ai,n (xn,k ) Then yˆk = c1 with P(c1 |ri ) . . 16 l4 l5 l6 l8 l9 small 1 med large small med 1 large x2 small x3 med large x5 Fig. 1 A fuzzy decision tree and rules as partitions of attributes where i is the index of the rule (i = 1, 2 . .
This paper includes such study and proposes a new supervised clustering algorithm for providing informative input information for decision tree induction algorithms. The FID algorithm is applied for the induction of the decision tree . This method has the advantage that the resulted fuzzy decision tree can be transformed into a fuzzy rule based classifier and the classification performance and the complexity of this model can fine-tuned by a rule pruning algorithm. Therefore, as the experimental study in this paper will illustrate, compact, perspicuous and accurate fuzzy classifier models can be identified by this approach.
Take output constraints into consideration, a numerical MPC algorithm can be used. Such an algorithm is formulated using the prediction which in the considered case is described by: y = ~y + Ak ⋅ Δu, (18) where Ak is given by (13) and the free response ~y is described by (14). Then a quadratic optimization problem, to be solved on–line at each sampling instant, is formulated. In this problem, in order to obtain the optimal Δu vector, the performance index (1) is minimized numerically, subject to the following constraints: Δumin ≤ Δu ≤ Δumax, umin ≤ u ≤ umax, ymin ≤ y ≤ ymax, [ ] (19) T where u = u k |k , … , u k + s −1|k , Δumin, Δumax, umin, umax, ymin, ymax are vectors of lower and upper limits of future changes and values of the manipulated variable and on predicted values of the output variable, respectively.