Results 11 to 20 of about 401,816 (284)
Надежность и качество сложных систем, 2022 Background. In the conditions of modern military conflicts, the problem of effective control of large groups of robots is actualized. The large amount of data associated with the operation of robotic systems (RC) determines the existence of ...
S.V. Ivanov +3 more
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Line spectrum extraction method based on hidden Markov model
Electronics Letters, 2021 To solve the difficult problem of line spectrum detection with a low signal‐to‐noise ratio, a line spectrum extraction algorithm based on a hidden Markov model (HMM) is proposed.
Kai Ma +4 more
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Geometric branching reproduction Markov processes
Modern Stochastics: Theory and Applications, 2020 We present a model of a continuous-time Markov branching process with the infinitesimal generating function defined by the geometric probability distribution.
Assen Tchorbadjieff, Penka Mayster
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Controlled Discrete-Time Semi-Markov Random Evolutions and Their Applications
Mathematics, 2021 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.
Anatoliy Swishchuk, Nikolaos Limnios
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Markov Processes in Data Center Networks
IEEE Access, 2021 A data center network is an important infrastructure in various applications of modern information technologies. Data centers store files with useful information, but the lifetime of these data centers is limited.
Fan-Qi Ma, Rui-Na Fan
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Synchronizing Objectives for Markov Decision Processes [PDF]
Electronic Proceedings in Theoretical Computer Science, 2011 We introduce synchronizing objectives for Markov decision processes (MDP). Intuitively, a synchronizing objective requires that eventually, at every step there is a state which concentrates almost all the probability mass.
Mahsa Shirmohammadi +2 more
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Feature Markov Decision Processes [PDF]
, 2008 General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). So far it is
Hutter, Marcus
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Estimation and control using sampling-based Bayesian reinforcement learning
IET Cyber-Physical Systems, 2019 Real-world autonomous systems operate under uncertainty about both their pose and dynamics. Autonomous control systems must simultaneously perform estimation and control tasks to maintain robustness to changing dynamics or modelling errors.
Patrick Slade +3 more
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Abstraction-Based Planning for Uncertainty-Aware Legged Navigation
IEEE Open Journal of Control Systems, 2023 This article addresses the problem of temporal-logic-based planning for bipedal robots in uncertain environments. We first propose an Interval Markov Decision Process abstraction of bipedal locomotion (IMDP-BL). Motion perturbations from multiple sources
Jesse Jiang, Samuel Coogan, Ye Zhao
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Rank-Driven Markov Processes [PDF]
Journal of Statistical Physics, 2011 32 pages, 2 colour ...
Grinfeld, Michael +2 more
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