This work focuses on the basic stochastic decomposition (SD) algorithm of Higle and Sen [J.L. Higle, S. Sen, Stochastic Decomposition, Kluwer Academic Publishers, 1996] for two-stage stochastic linear programming problems with complete recourse. The algorithm uses sampling when the random variables are represented by continuous distribution functions. Traditionally, this method has been applied...

In this paper, we develop a simulation-based approach for two-stage stochastic programs with recourse. We construct an augmented probability model with stochastic shocks and decision variables. Simulating from the augmented probability model solves for the expected recourse function and the optimal first-stage decision. Markov chain Monte Carlo methods, together with ergodic averaging, provide ...

In the development of a speech understanding system, the recourse to stochastic techniques can greatly reduce the need for human expertise. A known disadvantage is that stochastic models require large annotated training corpora in order to reliably estimate model parameters. Manual semantic annotation of such corpora is tedious, expensive, and subject to inconsistencies. In order to decrease th...

a fuzzy observer based scheme for synchronizing two hyperchaoticoscillators via a scalar transmitted signal for cryptographic application isproposed. the takagi-sugeno fuzzy model exactly represents chaotic systems.based on the general fuzzy model, the fuzzy observer of a chaotic system isdesigned on the basis of the n-shift multiple state based key encryption algorithm.the scalar transmitted s...

In this paper, we formulate a multiobjective fuzzy random simple recourse programming problem, in which fuzzy random variables coefficients are involved in equality constraints. In the proposed method, equality constraints with fuzzy random variables are defined on the basis of a possibility measure and and a two-stage programming method. For a given permissible possibility level specified by t...

This paper discusses hybrid probabilistic and fuzzy set approaches to propagating randomness and imprecision in risk assessment and fuzzy time series models. Stochastic and Computational Intelligence methods, such as Probability bounds analysis, Fuzzy -levels analysis, Fuzzy random vectors, Wavelets decomposition and Wavelets Networks are combined to capture different kinds of uncertainty. The...

h a stochastic and fuzzy environment, a multiobjective fuzzy stopping problem is discussed. The randomness and fuzziness are evaluated by probabilistic expectations and linear ranking functions, respectively. Pareto optimal fuzzy stopping times are given under the assumption of regularity for stopping rules, by using X-optimal stopping times. @ 2003 Elsevier Ltd. All rights reserved. &Fords-Mul...

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

In this paper, we focus on multiobjective fuzzy random programming problems with simple recourse through a fractile optimization model, in which fuzzy random variables coefficients are involved in equality constraints, and random variables coefficients are involved in the objective functions. In the proposed method, equality constraints with fuzzy random variables are defined on the basis of a ...