Relevant bibliographies by topics / Discrete-time systems. Stochastic control theory. Markov processes

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Discrete-time systems. Stochastic control theory. Markov processes'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Discrete-time systems. Stochastic control theory. Markov processes"

1

LEŚNIAK, KRZYSZTOF. "ON DISCRETE STOCHASTIC PROCESSES WITH DISJUNCTIVE OUTCOMES." Bulletin of the Australian Mathematical Society 90, no. 1 (2014): 149–59. http://dx.doi.org/10.1017/s0004972714000124.

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AbstractWe introduce a class of discrete-time stochastic processes, called disjunctive processes, which are important for reliable simulations in random iteration algorithms. Their definition requires that all possible patterns of states appear with probability 1. Sufficient conditions for nonhomogeneous chains to be disjunctive are provided. Suitable examples show that strongly mixing Markov chains and pairwise independent sequences, often employed in applications, may not be disjunctive. As a particular step towards a general theory we shall examine the problem arising when disjunctiveness i
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Dshalalow, Jewgeni. "Multichannel queueing systems with infinite waiting room and stochastic control." Journal of Applied Probability 26, no. 2 (1989): 345–62. http://dx.doi.org/10.2307/3214040.

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A wide class of multichannel queueing models appears to be useful in practice where the input stream of customers can be controlled at the moments preceding the customers' departures from the source (e.g. airports, transportation systems, inventories, tandem queues). In addition, the servicing facility can govern the intensity of the servicing process that further improves flexibility of the system. In such a multichannel queue with infinite waiting room the queueing process {Zt; t ≧ 0} is under investigation. The author obtains explicit formulas for the limiting distribution of (Zt) partly us
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Dshalalow, Jewgeni. "Multichannel queueing systems with infinite waiting room and stochastic control." Journal of Applied Probability 26, no. 02 (1989): 345–62. http://dx.doi.org/10.1017/s0021900200027339.

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A wide class of multichannel queueing models appears to be useful in practice where the input stream of customers can be controlled at the moments preceding the customers' departures from the source (e.g. airports, transportation systems, inventories, tandem queues). In addition, the servicing facility can govern the intensity of the servicing process that further improves flexibility of the system. In such a multichannel queue with infinite waiting room the queueing process {Zt ; t ≧ 0} is under investigation. The author obtains explicit formulas for the limiting distribution of (Zt ) partly
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4

Khan, Inam Ullah, Asrin Abdollahi, Ryan Alturki, et al. "Intelligent Detection System Enabled Attack Probability Using Markov Chain in Aerial Networks." Wireless Communications and Mobile Computing 2021 (September 9, 2021): 1–9. http://dx.doi.org/10.1155/2021/1542657.

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The Internet of Things (IoT) plays an important role to connect people, data, processes, and things. From linked supply chains to big data produced by a large number of IoT devices to industrial control systems where cybersecurity has become a critical problem in IoT-powered systems. Denial of Service (DoS), distributed denial of service (DDoS), and ping of death attacks are significant threats to flying networks. This paper presents an intrusion detection system (IDS) based on attack probability using the Markov chain to detect flooding attacks. While the paper includes buffer queue length by
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Swishchuk, Anatoliy, and Nikolaos Limnios. "Controlled Discrete-Time Semi-Markov Random Evolutions and Their Applications." Mathematics 9, no. 2 (2021): 158. http://dx.doi.org/10.3390/math9020158.

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In this paper, we introduced controlled discrete-time semi-Markov random evolutions. These processes are random evolutions of discrete-time semi-Markov processes where we consider a control. applied to the values of random evolution. The main results concern time-rescaled weak convergence limit theorems in a Banach space of the above stochastic systems as averaging and diffusion approximation. The applications are given to the controlled additive functionals, controlled geometric Markov renewal processes, and controlled dynamical systems. We provide dynamical principles for discrete-time dynam
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Sanger, Terence D. "Distributed Control of Uncertain Systems Using Superpositions of Linear Operators." Neural Computation 23, no. 8 (2011): 1911–34. http://dx.doi.org/10.1162/neco_a_00151.

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Control in the natural environment is difficult in part because of uncertainty in the effect of actions. Uncertainty can be due to added motor or sensory noise, unmodeled dynamics, or quantization of sensory feedback. Biological systems are faced with further difficulties, since control must be performed by networks of cooperating neurons and neural subsystems. Here, we propose a new mathematical framework for modeling and simulation of distributed control systems operating in an uncertain environment. Stochastic differential operators can be derived from the stochastic differential equation d
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GÖRNERUP, OLOF, and MARTIN NILSSON JACOBI. "A METHOD FOR INFERRING HIERARCHICAL DYNAMICS IN STOCHASTIC PROCESSES." Advances in Complex Systems 11, no. 01 (2008): 1–16. http://dx.doi.org/10.1142/s0219525908001507.

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Complex systems may often be characterized by their hierarchical dynamics. In this paper we present a method and an operational algorithm that automatically infer this property in a broad range of systems — discrete stochastic processes. The main idea is to systematically explore the set of projections from the state space of a process to smaller state spaces, and to determine which of the projections impose Markovian dynamics on the coarser level. These projections, which we call Markov projections, then constitute the hierarchical dynamics of the system. The algorithm operates on time series
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Qiu, Li, Qin Luo, Shanbin Li, and Bugong Xu. "Modeling and Output Feedback Control of Networked Control Systems with Both Time Delays; and Packet Dropouts." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/609236.

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This paper is concerned with the problem of modeling and output feedback controller design for a class of discrete-time networked control systems (NCSs) with time delays and packet dropouts. A Markovian jumping method is proposed to deal with random time delays and packet dropouts. Different from the previous studies on the issue, the characteristics of networked communication delays and packet dropouts can be truly reflected by the unified model; namely, both sensor-to-controller (S-C) and controller-to-actuator (C-A) time delays, and packet dropouts are modeled and their history behavior is
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Wu, Yongbao, Haotian Pi, and Wenxue Li. "Feedback control based on discrete-time state observations for stabilization of coupled regime-switching jump diffusion with Markov switching topologies." IMA Journal of Mathematical Control and Information 37, no. 4 (2020): 1423–46. http://dx.doi.org/10.1093/imamci/dnaa019.

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Abstract In this paper, the stabilization of coupled regime-switching jump diffusion with Markov switching topologies (CRJDM) is discussed. Particularly, we remove the restrictions that each of the switching subnetwork topologies is strongly connected or contains a directed spanning tree. Furthermore, a feedback control based on discrete-time state observations is proposed to make the CRJDM asymptotically stable. In most existing literature, feedback control only depends on discrete-time observations of state processes, while switching processes are observed continuously. Different from previo
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ANNUNZIATO, M., and A. BORZÌ. "Optimal control of a class of piecewise deterministic processes." European Journal of Applied Mathematics 25, no. 1 (2013): 1–25. http://dx.doi.org/10.1017/s0956792513000259.

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A new control strategy for a class of piecewise deterministic processes (PDP) is presented. In this class, PDP stochastic processes consist of ordinary differential equations that are subject to random switches corresponding to a discrete Markov process. The proposed strategy aims at controlling the probability density function (PDF) of the PDP. The optimal control formulation is based on the hyperbolic Fokker–Planck system that governs the time evolution of the PDF of the PDP and on tracking objectives of terminal configuration with a target PDF. The corresponding optimization problems are fo
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Dissertations / Theses on the topic "Discrete-time systems. Stochastic control theory. Markov processes"

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Tello, Oquendo Luis Patricio. "Design and Performance Analysis of Access Control Mechanisms for Massive Machine-to-Machine Communications in Wireless Cellular Networks." Doctoral thesis, Universitat Politècnica de València, 2018. http://hdl.handle.net/10251/107946.

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En la actualidad, la Internet de las Cosas (Internet of Things, IoT) es una tecnología esencial para la próxima generación de sistemas inalámbricos. La conectividad es la base de IoT, y el tipo de acceso requerido dependerá de la naturaleza de la aplicación. Uno de los principales facilitadores del entorno IoT es la comunicación machine-to-machine (M2M) y, en particular, su enorme potencial para ofrecer conectividad ubicua entre dispositivos inteligentes. Las redes celulares son la elección natural para las aplicaciones emergentes de IoT y M2M. Un desafío importante en las redes celulares es c
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Hsu, Shun-pin. "Discrete-time partially observed Markov decision processes ergodic, adaptive, and safety control /." Thesis, 2002. http://wwwlib.umi.com/cr/utexas/fullcit?p3110619.

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El-Khatib, Mayar. "Highway Development Decision-Making Under Uncertainty: Analysis, Critique and Advancement." Thesis, 2010. http://hdl.handle.net/10012/5741.

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While decision-making under uncertainty is a major universal problem, its implications in the field of transportation systems are especially enormous; where the benefits of right decisions are tremendous, the consequences of wrong ones are potentially disastrous. In the realm of highway systems, decisions related to the highway configuration (number of lanes, right of way, etc.) need to incorporate both the traffic demand and land price uncertainties. In the literature, these uncertainties have generally been modeled using the Geometric Brownian Motion (GBM) process, which has been used extens
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Books on the topic "Discrete-time systems. Stochastic control theory. Markov processes"

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Costa, Oswaldo L. V. Continuous-Time Markov Jump Linear Systems. Springer Berlin Heidelberg, 2013.

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Hernández-Lerma, O. Further topics on discrete-time Markov control processes. Springer, 1999.

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Söderström, Torsten. Discrete-time stochastic systems: Estimation and control. Prentice Hall, 1994.

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Aoki, Masanao. Optimization of stochastic systems: Topics in discrete-time dynamics. 2nd ed. Academic Press, 1989.

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Optimization of stochastic systems: Topics in discrete-time dynamics. 2nd ed. Academic Press, 1989.

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Toader, Morozan, Stoica Adrian, and SpringerLink (Online service), eds. Mathematical Methods in Robust Control of Discrete-Time Linear Stochastic Systems. Springer Science+Business Media, LLC, 2010.

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Alberto, Achcar Jorge, and SpringerLink (Online service), eds. Applications of Discrete-time Markov Chains and Poisson Processes to Air Pollution Modeling and Studies. Springer New York, 2013.

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O, Seppäläinen Timo, ed. A course on large deviations with an introduction to Gibbs measures. American Mathematical Society, 2015.

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9

Schurz, Henri, Philip J. Feinsilver, Gregory Budzban, and Harry Randolph Hughes. Probability on algebraic and geometric structures: International research conference in honor of Philip Feinsilver, Salah-Eldin A. Mohammed, and Arunava Mukherjea, June 5-7, 2014, Southern Illinois University, Carbondale, Illinois. Edited by Mohammed Salah-Eldin 1946- and Mukherjea Arunava 1941-. American Mathematical Society, 2016.

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Discrete-Time Markov Jump Linear Systems (Probability and its Applications). Springer, 2004.

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Book chapters on the topic "Discrete-time systems. Stochastic control theory. Markov processes"

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Lozovanu, Dmitrii, and Stefan Pickl. "Discrete Stochastic Processes, Numerical Methods for Markov Chains and Polynomial Time Algorithms." In Optimization of Stochastic Discrete Systems and Control on Complex Networks. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11833-8_1.

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Lozovanu, Dmitrii, and Stefan Pickl. "Stochastic Optimal Control Problems and Markov Decision Processes with Infinite Time Horizon." In Optimization of Stochastic Discrete Systems and Control on Complex Networks. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11833-8_2.

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Conference papers on the topic "Discrete-time systems. Stochastic control theory. Markov processes"

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Malikopoulos, Andreas A. "A Rollout Control Algorithm for Discrete-Time Stochastic Systems." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4047.

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The growing demand for making autonomous intelligent systems that can learn how to improve their performance while interacting with their environment has induced significant research on computational cognitive models. Computational intelligence, or rationality, can be achieved by modeling a system and the interaction with its environment through actions, perceptions, and associated costs. A widely adopted paradigm for modeling this interaction is the controlled Markov chain. In this context, the problem is formulated as a sequential decision-making process in which an intelligent system has to
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