Dynamic systems in many branches of science and industry are often perturbed by various types of environmental noise. Analysis of this class of models are very popular among researchers. In this paper, we present a method for approximating solution of fractional-order stochastic delay differential equations driven by Brownian motion. The fractional derivatives are considered in the Caputo sense...

this article aims at designing the mathematical model for aras dam reservoir in time operation with missions of agriculture needs safeguarding, water electric energy production and torrent controls. reservoir system relations determinate and these relations at framework of are determined and stochastic dynamic programming are calculated executive results of this model are presentation of guides...

Abstract : In this paper , we apply the stochastic dynamic programming to approximate the mean project completion time in dynamic Markov PERT networks. It is assumed that the activity durations are independent random variables with exponential distributions, but some social and economical problems influence the mean of activity durations. It is also assumed that the social problems evolve in ac...

The Pontrjagin maximum principle solves the problem of optimal control of a continuous deterministic system. The discrete maximum principle solves the problem of optimal control of a discrete-time deterministic system. The maximum principle changes the problem of optimal control to a two point boundary value problem which can be completely solved only in special tasks. It was probably the reaso...

A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment (SRUC) are viewed as first-stage decisions, and dispatch is relegated to the second stage...

Many real world problems involve making repeated decisions over time in an uncertain environment. These decisions often involve a trade-off between some immediate benefit(s) and possible future benefit(s), and also take in to account the impact the decision will have on future decisions and benefits. Stochastic dynamic programming (SDP) is often used to analyse problems of this type and the obj...

This paper discusses the optimal preview control problem for a class of linear continuous stochastic control systems in the infinite horizon, based on the augmented error system method. Firstly, an assistant system is designed and the state equation is translated to the assistant system.Then, an integrator is introduced to construct a stochastic augmented error system. As a result, the tracking...

We provide an American version of the Geometric Dynamic Programming Principle of Soner and Touzi [22] for stochastic target problems. This opens the doors to a wide range of applications, particularly in risk control in finance and insurance, in which a controlled stochastic process has to be maintained in a given set on a time interval [0, T ]. As an example of application, we show how it can ...

Unnecessarily conservative behavior of standard process control techniques can be avoided by stochastic programming models when the distribution of random distur­ bances is known. In an earlier study we have investigated such an approach for tank level constraints of a distillation process. Here we address techniques that have accelerated the numerical solution of the large and expensive stocha...

in this paper at rst, a history of mathematical models is given.next, some basic information about random variables, stochastic processesand markov chains is introduced. as follows, the entropy for a discrete timemarkov process is mentioned. after that, the entropy for sis stochastic modelsis computed, and it is proved that an epidemic will be disappeared after a longtime.