5,723 research outputs found
Stability of stochastic impulsive differential equations: integrating the cyber and the physical of stochastic systems
According to Newton's second law of motion, we humans describe a dynamical
system with a differential equation, which is naturally discretized into a
difference equation whenever a computer is used. The differential equation is
the physical model in human brains and the difference equation the cyber model
in computers for the dynamical system. The physical model refers to the
dynamical system itself (particularly, a human-designed system) in the physical
world and the cyber model symbolises it in the cyber counterpart. This paper
formulates a hybrid model with impulsive differential equations for the
dynamical system, which integrates its physical model in real world/human
brains and its cyber counterpart in computers. The presented results establish
a theoretic foundation for the scientific study of control and communication in
the animal/human and the machine (Norbert Wiener) in the era of rise of the
machines as well as a systems science for cyber-physical systems (CPS)
On almost sure stability of hybrid stochastic systems with mode-dependent interval delays
This note develops a criterion for almost sure stability of hybrid stochastic systems with mode-dependent interval time delays, which improves an existing result by exploiting the relation between the bounds of the time delays and the generator of the continuous-time Markov chain. The improved result shows that the presence of Markovian switching is quite involved in the stability analysis of delay systems. Numerical examples are given to verify the effectiveness
A Stochastic Gradient Method with Mesh Refinement for PDE Constrained Optimization under Uncertainty
Models incorporating uncertain inputs, such as random forces or material
parameters, have been of increasing interest in PDE-constrained optimization.
In this paper, we focus on the efficient numerical minimization of a convex and
smooth tracking-type functional subject to a linear partial differential
equation with random coefficients and box constraints. The approach we take is
based on stochastic approximation where, in place of a true gradient, a
stochastic gradient is chosen using one sample from a known probability
distribution. Feasibility is maintained by performing a projection at each
iteration. In the application of this method to PDE-constrained optimization
under uncertainty, new challenges arise. We observe the discretization error
made by approximating the stochastic gradient using finite elements. Analyzing
the interplay between PDE discretization and stochastic error, we develop a
mesh refinement strategy coupled with decreasing step sizes. Additionally, we
develop a mesh refinement strategy for the modified algorithm using iterate
averaging and larger step sizes. The effectiveness of the approach is
demonstrated numerically for different random field choices
Exponential Stabilisation of Continuous-time Periodic Stochastic Systems by Feedback Control Based on Periodic Discrete-time Observations
Since Mao in 2013 discretised the system observations for stabilisation problem of hybrid SDEs (stochastic differential equations with Markovian switching) by feedback control, the study of this topic using a constant observation frequency has been further developed. However, time-varying observation frequencies have not been considered. Particularly, an observational more efficient way is to consider the time-varying property of the system and observe a periodic SDE system at the periodic time-varying frequencies. This study investigates how to stabilise a periodic hybrid SDE by a periodic feedback control, based on periodic discrete-time observations. This study provides sufficient conditions under which the controlled system can achieve pth moment exponential stability for p > 1 and almost sure exponential stability. Lyapunov's method and inequalities are main tools for derivation and analysis. The existence of observation interval sequences is verified and one way of its calculation is provided. Finally, an example is given for illustration. Their new techniques not only reduce observational cost by reducing observation frequency dramatically but also offer flexibility on system observation settings. This study allows readers to set observation frequencies according to their needs to some extent
Edge of Chaos and Genesis of Turbulence
The edge of chaos is analyzed in a spatially extended system, modeled by the
regularized long-wave equation, prior to the transition to permanent
spatiotemporal chaos. In the presence of coexisting attractors, a chaotic
saddle is born at the basin boundary due to a smooth-fractal metamorphosis. As
a control parameter is varied, the chaotic transient evolves to well-developed
transient turbulence via a cascade of fractal-fractal metamorphoses. The edge
state responsible for the edge of chaos and the genesis of turbulence is an
unstable travelling wave in the laboratory frame, corresponding to a saddle
point lying at the basin boundary in the Fourier space
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