A random geometric graph Gn is obtained as follows. We takeX1, X2, . . . , Xn ∈ R at random (i.i.d. according to some probability distribution ν on R). For i 6= j we joinXi andXj by an edge if ‖Xi−Xj ‖< r(n). We study the properties of the chromatic number χn and clique number ωn of this graph as n becomes large, where we assume that r(n) → 0. We allow any choice ν that has a bounded density fu...

Much has been written on variable exponent spaces in recent years. Most of the literature deals with normed space structure such spaces. However, because variability exponent, underlying modular these is radically different from that induced by norm. In this article, we focus our attention progress made toward study sequence Lebesgue exponents. particular, present a survey state art regarding g...

support vector regression (svr) solves regression problems based on the concept of support vector machine (svm). in this paper, a new model of svr with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...

Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, a new model of SVR with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. Using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...

in this paper, a model of an optimal control problem with chance constraints is introduced. the parametersof the constraints are fuzzy, random or fuzzy random variables. todefuzzify the constraints, we consider possibility levels.  bychance-constrained programming the chance constraints are converted to crisp constraints which are neither fuzzy nor stochastic and then the resulting classical op...