Chance-constrained programming with fuzzy stochastic coefficients

cedure involves, for each sample, κ (m) (x), two main steps: the set-up and the resolution. In the set-up step, one goes through the subdomains to a) construct the local operator of

Then, a convergence result is proved by using the general framework of Barles-Souganidis [10] based on the monotonicity, sta- bility, consistency of the scheme (a precise definition

Both approaches will lead us to consider algorithms such as (6) in which either a smooth approximation θ b of function θ will be used in both equations (6a) and (6b), or

Any/e CR"" can be regarded as a distribution e (^^ and arguments similar to those at the end of Section 5 lead to the following criterion : i° A sufficient condition for fe CR

If the leader’s strategy is scalar, the leader’s problem can be solved using methods of one-dimensional optimization..

We study the long time behavior of the solution of a stochastic PDEs with random coefficients assuming that randomness arises in a different independent scale.. We apply the

Intenting to enumerate the problems raised when setting a fuzzy controller, we examine then several experiments with numeric or symbolic formalizations about fuzzy control from two

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