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State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays
This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this Letter, we investigate the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters as well as mode-dependent mixed time-delays. The parameters of the discrete-time neural networks are subject to the switching from one mode to another at different times according to a Markov chain, and the mixed time-delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. New techniques are developed to deal with the mixed time-delays in the discrete-time setting, and a novel Lyapunov–Krasovskii functional is put forward to reflect the mode-dependent time-delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A numerical example is exploited to show the usefulness of the derived LMI-based conditions.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK under Grants BB/C506264/1 and 100/EGM17735, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grants GR/S27658/01 and EP/C524586/1, an International Joint Project sponsored by the Royal Society of the UK, the Natural Science Foundation of Jiangsu Province of China under Grant BK2007075, the National Natural Science Foundation of China under Grant 60774073, and the Alexander von Humboldt Foundation of Germany
Evidence for collapsing fields in corona and photosphere during the 15 February 2011 X2.2 flare: SDO AIA and HMI Observations
We use high-resolution images of the sun obtained by the SDO/AIA instrument
to study the evolution of the coronal loops in a flaring solar active region.
During 15 February 2011 a X-2.2 class flare occurred in NOAA 11158, a
sunspot complex. We identify three distinct phases of the
coronal loop dynamics during this event: (i) {\it Slow rise phase}: slow rising
motion of the loop-tops prior to the flare in response to slow rise of the
underlying flux rope, (ii) {\it Collapse phase}: sudden contraction of the
loop-tops with lower loops collapsing earlier than the higher loops, and (iii)
{\it Oscillation phase}: the loops exhibit global kink oscillations after the
collapse phase at different periods, with period decreasing with decreasing
height of the loops. The period of these loop oscillations is used to estimate
the field strength in the coronal loops of different loop lengths in this
active region. Further, we also use SDO/HMI observations to study the
photospheric changes close to the polarity inversion line (PIL). The
longitudinal magnetograms show step-wise permanent decrease in the magnetic
flux after the flare over a coherent patch along the PIL. Further, we examine
the HMI Stokes I,Q,U,V profiles over this patch and find that the Stokes-V
signal systematically decreases while the Stokes-Q and U signal increases after
the flare. These observations suggest that close to the PIL the field
configuration became more horizontal after the flare. We also use HMI vector
magnetic field observations to quantify the changes in the field inclination
angle and found an inward collapse of the field lines towards the polarity
inversion line (PIL) by 10. These observations are consistent
with the "coronal implosion" scenario and its predictions about flare related
photospheric field changes.Comment: 27 pages, 7 figures, in press (Astrophysical Journal
Contracting and Erupting Components of Sigmoidal Active Regions
It is recently noted that solar eruptions can be associated with the
contraction of coronal loops that are not involved in magnetic reconnection
processes. In this paper, we investigate five coronal eruptions originating
from four sigmoidal active regions, using high-cadence, high-resolution
narrowband EUV images obtained by the Solar Dynamic Observatory (SDO}). The
magnitudes of the flares associated with the eruptions range from the
GOES-class B to X. Owing to the high-sensitivity and broad temperature coverage
of the Atmospheric Imaging Assembly (AIA) onboard SDO, we are able to identify
both the contracting and erupting components of the eruptions: the former is
observed in cold AIA channels as the contracting coronal loops overlying the
elbows of the sigmoid, and the latter is preferentially observed in warm/hot
AIA channels as an expanding bubble originating from the center of the sigmoid.
The initiation of eruption always precedes the contraction, and in the
energetically mild events (B and C flares), it also precedes the increase in
GOES soft X-ray fluxes. In the more energetic events, the eruption is
simultaneous with the impulsive phase of the nonthermal hard X-ray emission.
These observations confirm the loop contraction as an integrated process in
eruptions with partially opened arcades. The consequence of contraction is a
new equilibrium with reduced magnetic energy, as the contracting loops never
regain their original positions. The contracting process is a direct
consequence of flare energy release, as evidenced by the strong correlation of
the maximal contracting speed, and strong anti-correlation of the time delay of
contraction relative to expansion, with the peak soft X-ray flux. This is also
implied by the relationship between contraction and expansion, i.e., their
timing and speed.Comment: Accepted for publication in Ap
Exponential stabilization of a class of stochastic system with Markovian jump parameters and mode-dependent mixed time-delays
Copyright [2010] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected].
By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this technical note, the globally exponential stabilization problem is investigated for a general class of stochastic systems with both Markovian jumping parameters and mixed time-delays. The mixed mode-dependent time-delays consist of both discrete and distributed delays. We aim to design a memoryless state feedback controller such that the closed-loop system is stochastically exponentially stable in the mean square sense. First, by introducing a new Lyapunov-Krasovskii functional that accounts for the mode-dependent mixed delays, stochastic analysis is conducted in order to derive a criterion for the exponential stabilizability problem. Then, a variation of such a criterion is developed to facilitate the controller design by using the linear matrix inequality (LMI) approach. Finally, it is shown that the desired state feedback controller can be characterized explicitly in terms of the solution to a set of LMIs. Numerical simulation is carried out to demonstrate the effectiveness of the proposed methods.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., the National 973 Program of China under Grant 2009CB320600, and the Alexander von Humboldt Foundation of Germany. Recommended by Associate Editor G. Chesi
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
This paper was motivated by the problem of how to make robots fuse and
transfer their experience so that they can effectively use prior knowledge and
quickly adapt to new environments. To address the problem, we present a
learning architecture for navigation in cloud robotic systems: Lifelong
Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge
fusion algorithm for upgrading a shared model deployed on the cloud. Then,
effective transfer learning methods in LFRL are introduced. LFRL is consistent
with human cognitive science and fits well in cloud robotic systems.
Experiments show that LFRL greatly improves the efficiency of reinforcement
learning for robot navigation. The cloud robotic system deployment also shows
that LFRL is capable of fusing prior knowledge. In addition, we release a cloud
robotic navigation-learning website based on LFRL
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