1,673 research outputs found
Stability analysis of markovian jump systems with multiple delay components and polytopic uncertainties
This paper investigates the stability problem of Markovian jump systems with multiple delay components and polytopic uncertainties. A new Lyapunov-Krasovskii functional is used for the stability analysis of Markovian jump systems with or without polytopic uncertainties. Two numerical examples are provided to demonstrate the applicability of the proposed approach. Ā© Springer Science+Business Media, LLC 2011.published_or_final_versio
Order Reduction of the Chemical Master Equation via Balanced Realisation
We consider a Markov process in continuous time with a finite number of
discrete states. The time-dependent probabilities of being in any state of the
Markov chain are governed by a set of ordinary differential equations, whose
dimension might be large even for trivial systems. Here, we derive a reduced
ODE set that accurately approximates the probabilities of subspaces of interest
with a known error bound. Our methodology is based on model reduction by
balanced truncation and can be considerably more computationally efficient than
the Finite State Projection Algorithm (FSP) when used for obtaining transient
responses. We show the applicability of our method by analysing stochastic
chemical reactions. First, we obtain a reduced order model for the
infinitesimal generator of a Markov chain that models a reversible,
monomolecular reaction. In such an example, we obtain an approximation of the
output of a model with 301 states by a reduced model with 10 states. Later, we
obtain a reduced order model for a catalytic conversion of substrate to a
product; and compare its dynamics with a stochastic Michaelis-Menten
representation. For this example, we highlight the savings on the computational
load obtained by means of the reduced-order model. Finally, we revisit the
substrate catalytic conversion by obtaining a lower-order model that
approximates the probability of having predefined ranges of product molecules.Comment: 12 pages, 6 figure
Synchronization of coupled neutral-type neural networks with jumping-mode-dependent discrete and unbounded distributed delays
This is the post-print version of the Article. The official published version can be accessed from the links below - Copyright @ 2013 IEEE.In this paper, the synchronization problem is studied for an array of N identical delayed neutral-type neural networks with Markovian jumping parameters. The coupled networks involve both the mode-dependent discrete-time delays and the mode-dependent unbounded distributed time delays. All the network parameters including the coupling matrix are also dependent on the Markovian jumping mode. By introducing novel Lyapunov-Krasovskii functionals and using some analytical techniques, sufficient conditions are derived to guarantee that the coupled networks are asymptotically synchronized in mean square. The derived sufficient conditions are closely related with the discrete-time delays, the distributed time delays, the mode transition probability, and the coupling structure of the networks. The obtained criteria are given in terms of matrix inequalities that can be efficiently solved by employing the semidefinite program method. Numerical simulations are presented to further demonstrate the effectiveness of the proposed approach.This work was supported in part by the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 61074129, 61174136 and 61134009, and the Natural Science Foundation of Jiangsu Province of China under Grants BK2010313 and BK2011598
Tractable nonlinear memory functions as a tool to capture and explain dynamical behaviours
Mathematical approaches from dynamical systems theory are used in a range of
fields. This includes biology where they are used to describe processes such as
protein-protein interaction and gene regulatory networks. As such networks
increase in size and complexity, detailed dynamical models become cumbersome,
making them difficult to explore and decipher. This necessitates the
application of simplifying and coarse graining techniques in order to derive
explanatory insight. Here we demonstrate that Zwanzig-Mori projection methods
can be used to arbitrarily reduce the dimensionality of dynamical networks
while retaining their dynamical properties. We show that a systematic expansion
around the quasi-steady state approximation allows an explicit solution for
memory functions without prior knowledge of the dynamics. The approach not only
preserves the same steady states but also replicates the transients of the
original system. The method also correctly predicts the dynamics of multistable
systems as well as networks producing sustained and damped oscillations.
Applying the approach to a gene regulatory network from the vertebrate neural
tube, a well characterised developmental transcriptional network, identifies
features of the regulatory network responsible dfor its characteristic
transient behaviour. Taken together, our analysis shows that this method is
broadly applicable to multistable dynamical systems and offers a powerful and
efficient approach for understanding their behaviour.Comment: (8 pages, 8 figures
Stability Analysis of Stochastic Markovian Jump Neural Networks with Different Time Scales and Randomly Occurred Nonlinearities Based on Delay-Partitioning Projection Approach
In this paper, the mean square asymptotic stability of stochastic Markovian jump neural networks with different time scales and randomly occurred nonlinearities is investigated. In terms of linear matrix inequality (LMI) approach and delay-partitioning projection technique, delay-dependent stability criteria are derived for the considered neural networks for cases with or without the information of the delay rates via new Lyapunov-Krasovskii functionals. We also obtain that the thinner the delay is partitioned, the more obviously the conservatism can be reduced. An example with simulation results is given to show the effectiveness of the proposed approach
Stochastic Hybrid Automata with delayed transitions to model biochemical systems with delays
To study the effects of a delayed immune-response on the growth of an immuno- genic neoplasm we introduce Stochastic Hybrid Automata with delayed transi- tions as a representation of hybrid biochemical systems with delays. These tran- sitions abstractly model unknown dynamics for which a constant duration can be estimated, i.e. a delay. These automata are inspired by standard Stochastic Hybrid Automata, and their semantics is given in terms of Piecewise Determin- istic Markov Processes. The approach is general and can be applied to systems where (i) components at low concentrations are modeled discretely (so to retain their intrinsic stochastic fluctuations), (ii) abundant component, e.g., chemical signals, are well approximated by mean-field equations (so to simulate them efficiently) and (iii) missing components are abstracted with delays. Via sim- ulations we show in our application that interesting delay-induced phenomena arise, whose quantification is possible in this new quantitative framewor
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