Stochastic process

About: Stochastic process is a research topic. Over the lifetime, 31227 publications have been published within this topic receiving 898736 citations. The topic is also known as: random process & stochastic processes.

Dynamic power management based on continuous-time Markov decision processes

Abstract: This paper introduces a continuous-time, controllable Markov process model of a power-managed system. The system model is composed of the corresponding stochastic models of the service queue and the service provider. The system environment is modeled by a stochastic service request process. The problem of dynamic power management in such a system is formulated as a policy optimization problem and solved using an efficient "policy iteration" algorithm. Compared to previous work on dynamic power management, our formulation allows better modeling of the various system components, the power-managed system as a whole, and its environment. In addition it captures dependencies between the service queue and service provider status. Finally, the resulting power management policy is asynchronous, hence it is more power-efficient and more useful in practice. Experimental results demonstrate the effectiveness of our policy optimization algorithm compared to a number of heuristic (time-out and N-policy) algorithms.

Approximation of linear constant systems

Abstract: In this paper two problems are considered, the problem of modeling a given constant linear system by a constant linear system of fixed lower order, and the problem of finding a filter of fixed order to estimate a time-invariant random process from a related time-invariant random process. A quadratic criterion is used to select the optimum system in both cases. It is shown that the filtering problem reduces to the problem of modeling the corresponding Wiener filter. Necessary conditions for a solution are developed and stated in terms of standard Wiener filter theory notation. Numerical solution of the equations embodying the necessary conditions is considered and several examples are presented.

An Introduction to Stochastic Processes in Physics

Abstract: Contents: Preface and Acknowledgments Chapter 1: Random Variables Chapter 2: Expected Values Chapter 3: Random Steps Chapter 4: Continuous Random Variables Chapter 5: Normal Variable Theorems Chapter 6: Einstein's Brownian Motion Chapter 7: Ornstein-Uhlenbeck Processes Chapter 8: Langevin's Brownian Motion Chapter 9: Other Physical Processes Chapter 10: Fluctuations without Dissipation Appendix A: "On the Theory of Brownian Motion," by Paul Langevin, translated by Anthony Gythiel Appendix B: Kinetic Equations Answers to Problems References Index

Synchronization in an array of linearly stochastically coupled networks with time delays

Abstract: In this paper, the complete synchronization problem is investigated in an array of linearly stochastically coupled identical networks with time delays. The stochastic coupling term, which can reflect a more realistic dynamical behavior of coupled systems in practice, is introduced to model a coupled system, and the influence from the stochastic noises on the array of coupled delayed neural networks is studied thoroughly. Based on a simple adaptive feedback control scheme and some stochastic analysis techniques, several sufficient conditions are developed to guarantee the synchronization in an array of linearly stochastically coupled neural networks with time delays. Finally, an illustrate example with numerical simulations is exploited to show the effectiveness of the theoretical results.

Scenario-Based Multiobjective Volt/Var Control in Distribution Networks Including Renewable Energy Sources

Abstract: This paper proposes a stochastic multiobjective framework for daily volt/var control (VVC), including hydroturbine, fuel cell, wind turbine, and photovoltaic powerplants The multiple objectives of the VVC problem to be minimized are the electrical energy losses, voltage deviations, total electrical energy costs, and total emissions of renewable energy sources and grid For this purpose, the uncertainty related to hourly load, wind power, and solar irradiance forecasts are modeled in a scenario-based stochastic framework A roulette wheel mechanism based on the probability distribution functions of these random variables is considered to generate the scenarios Consequently, the stochastic multiobjective VVC (SMVVC) problem is converted to a series of equivalent deterministic scenarios Furthermore, an Evolutionary Algorithm using the Modified Teaching-Learning-Algorithm (MTLA) is proposed to solve the SMVVC in the form of a mixed-integer nonlinear programming problem In the proposed algorithm, a new mutation method is taken into account in order to enhance the global searching ability and mitigate the premature convergence to local minima Finally, two distribution test feeders are considered as case studies to demonstrate the effectiveness of the proposed SMVVC