Results 11 to 20 of about 16,838,741 (322)
Control mechanisms for stochastic biochemical systems via computation of reachable sets [PDF]
Royal Society Open Science, 2017 Controlling the behaviour of cells by rationally guiding molecular processes is an overarching aim of much of synthetic biology. Molecular processes, however, are notoriously noisy and frequently nonlinear.
Eszter Lakatos, Michael P. H. Stumpf
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A Semi-Markov Dynamic Capital Injection Problem for Distressed Banks
Risks, 2023 Our study investigates the optimal dividend strategy for a bank, taking into account the potential for government capital injections. We explore different types of government interventions, such as liberal, transparent, or uncertain strategies, and ...
Luca Di Persio +2 more
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Optimal lock-down intensity: A stochastic pandemic control approach of path integral
Computational and Mathematical Biophysics, 2023 The aim of this article is to determine the optimal intensity of lock-down measures and vaccination rates to control the spread of coronavirus disease 2019. The study uses a stochastic susceptible-infected-recovered (SIR) model with infection dynamics. A
Pramanik Paramahansa
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Cross Apprenticeship Learning Framework: Properties and Solution Approaches
IEEE Open Journal of Control Systems, 2023 Apprenticeship learning is a framework in which an agent learns a policy to perform a given task in an environment using example trajectories provided by an expert. In the real world, one might have access to expert trajectories in different environments
Ashwin Aravind +2 more
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A direct approach to linear-quadratic stochastic control [PDF]
Opuscula Mathematica, 2017 A direct approach is used to solve some linear-quadratic stochastic control problems for Brownian motion and other noise processes. This direct method does not require solving Hamilton-Jacobi-Bellman partial differential equations or backward stochastic ...
Tyrone E. Duncan, Bozenna Pasik-Duncan
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Wasserstein Distributionally Robust Stochastic Control: A Data-Driven Approach [PDF]
IEEE Transactions on Automatic Control, 2018 Standard stochastic control methods assume that the probability distribution of uncertain variables is available. Unfortunately, in practice, obtaining accurate distribution information is a challenging task.
Insoon Yang
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Deep Neural Networks Algorithms for Stochastic Control Problems on Finite Horizon: Numerical Applications [PDF]
Methodology and Computing in Applied Probability, 2018 This paper presents several numerical applications of deep learning-based algorithms for discrete-time stochastic control problems in finite time horizon that have been introduced in Huré et al. (2018).
Achref Bachouch +3 more
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IEEE Access, 2021 In this article, a robust decentralized tracking control scheme for a large-scale unmanned aerial vehicle (UAV) formation team networked control system (NCS) is proposed to overcome a non-scalable or even infeasible design problem due to high ...
Min-Yen Lee +3 more
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Algorithms, 2020 In this paper we study the optimization of the discrete-time stochastic linear-quadratic (LQ) control problem with conic control constraints on an infinite horizon, considering multiplicative noises. Stochastic control systems can be formulated as Markov
Ruobing Xue, Xiangshen Ye, Weiping Wu
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Ranking and Selection as Stochastic Control [PDF]
IEEE Transactions on Automatic Control, 2017 Under a Bayesian framework, we formulate the fully sequential sampling and selection decision in statistical ranking and selection as a stochastic control problem, and derive the associated Bellman equation.
Yijie Peng +3 more
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