Abstract Space On The Rough Variable And Rough Planning

Rough set theory was initialized by Pawlak in 1982. It has been applied to decision analysis, data reduction, pattern recognition .electric power system and so on. In 2002, Trust theory was built by Baoding Liu. Trust theory is a branch of mathematics that studies the behavior of rough events. With the width of study in rough-set theory, just to study the real-valued rough variables is not satisfied other subjects and technical demands. In practice, rough variables may not be real number values, and their values may be continuous functions, bounded variation functions and so on. It seems necessary to consider some extensions of rough variables. So the concept of X-valued rough variables is introduced. In this paper, some properties of X-valued rough variables are showed. And the concept of x- convergence which is an extension of strong convergence and weak convergence is presented, and the x-convergence of x-valued rough variables is discussed.The uncertain programming was studied by many researchers such as Baoding Liu. The hybrid intelligent algorithm integrated by simulation, neural networks and genetic algorithm has been applied in solving the expected model, chance-constrained programming, and dependent-chanced programming with stochastic, fuzzy ,rough environment and so on. In this paper, the hybrid intelligent algorithm is improved. A new hybrid intelligent algorithm integrated by rough simulation, neural networks and simulated annealing is applied to optimize the production-inventory model of chance-constrained programming with rough environment.