Download Applied Computational Intelligence: Proceedings of the 6th by Pierre D'hondt, Etienne E Kerre, Da Ruan PDF

By Pierre D'hondt, Etienne E Kerre, Da Ruan

FLINS, initially an acronym for "Fuzzy good judgment and clever applied sciences in Nuclear Science", has now been prolonged to incorporate computational clever structures for utilized examine. FLINS 2004, is the 6th in a chain of overseas meetings, covers cutting-edge examine and improvement in utilized computational intelligence for utilized learn as a rule and for power/nuclear engineering specifically. This booklet provides the most recent study traits and destiny examine instructions within the box.

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Extra resources for Applied Computational Intelligence: Proceedings of the 6th International FlINS Conference, Blankenberge, Belgium, September 1-3, 2004

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With a valued map the implication algebra can be used for a super symbolic evaluation of the classical logic. With a suitable code of the symbols in the implication algebra, we can give a modal logic value to every symbol. A connection with the modal logic super valuation and the implication algebra will be usehl for a description of the L algebra with the traditional True, False logic values. Because meta-theory of uncertainty uses modal logic as logic instrument, we suggest in this paper to rewrite the main part of the implication algebra by modal logic in such a way as to write a common language between the traditional fuzzy logic and the implication algebra.

A. Orlovski: Calculus of Decomposable Properties, Fuzzy Sets and Decisions. Allerton Press, New York, 1994. 23. K. Pattanaik: Voting and Collective Choice. Cambdrige University Press, Cambridge, 1971. 24. B. Roy: Decision science or decision-aid science. European Journal of Operational Research 66 (1993), 184-203. 25. L. Savage: The Foundations of Statistics. Wiley, New York, 1954. 26. K. Sen: Collective Choice and Social Welfare. Holden-Day, San Francisco, 1970. 27. G. Shafer: Savage revisited (with discussion).

Nth-order ' higher-order neural unit with an n-dimensional input vector can be expressed as Y = 442) n n where x = [ X I , 2 2 , . . ,xn] E Rn'is the vector of neural inputs, y E R is an output scalar, and $( . ), exists. The structure of this higher-order neural unit is shown in Figure 1. 3 + w12x1x2 +w224 + w112+2 + W 1 2 2 X 1 4 + w 2 2 2 4 ) (3) Theses higher-order neural units can be used in conventional feedforward neural network structures as the hidden units to form higher-order neural networks.

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