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Stochastic Hybrid Systems: Application to Communication Networks (2004)

by J P Hespanha
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Stochastic Analysis of Gene Regulatory Networks Using Moment Closure

by Abhyudai Singh, João P. Hespanha , 2007
"... Random fluctuations in gene regulatory networks are inevitable due to the probabilistic nature of chemical reactions and the small populations of proteins, mRNAs present inside cells. These fluctuations are usually reported in terms of the first and second order statistical moments of the protein po ..."
Abstract - Cited by 7 (4 self) - Add to MetaCart
Random fluctuations in gene regulatory networks are inevitable due to the probabilistic nature of chemical reactions and the small populations of proteins, mRNAs present inside cells. These fluctuations are usually reported in terms of the first and second order statistical moments of the protein populations. If the birth-death rates of the mRNAs or the proteins are nonlinear, then the dynamics of these moments generally do not form a closed system of differential equations, in the sense that their time-derivatives depends on moments of order higher than two. Recent work has developed techniques to obtain the two lowest-order moments by closing their dynamics, which involves approximating the higher order moments as nonlinear functions of the two lowest ones. This paper uses these moment closure techniques to quantify noise in several gene regulatory networks. In gene expression mechanisms in which a protein inhibits its own transcription, the resulting negative feedback reduces stochastic variations in the protein populations. Often the protein itself is not active and combines with itself to form an active multimer, which them inhibits the transcription. We demonstrate that this more sophisticated form of negative feedback (using multimerization) is more effective in suppressing noise. We also consider a two-gene cascade activation network in which the protein expressed by one gene activates another gene to express a second protein. Analysis shows that the stochastic fluctuations in the population of the activated protein increases with the degree of multimerization in the activating protein.

Safety Analysis of Sugar Cataract Development Using Stochastic Hybrid Systems

by Derek Riley, Xenofon Koutsoukos, Kasandra Riley - HYBRID SYSTEMS: COMPUTATION AND CONTROL 2007 LNCS 4416 , 2007
"... Modeling and analysis of biochemical systems are critical problems because they can provide new insights into systems which can not be easily tested with real experiments. One such biochemical process is the formation of sugar cataracts in the lens of an eye. Analyzing the sugar cataract developmen ..."
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Modeling and analysis of biochemical systems are critical problems because they can provide new insights into systems which can not be easily tested with real experiments. One such biochemical process is the formation of sugar cataracts in the lens of an eye. Analyzing the sugar cataract development process is a challenging problem due to the highly-coupled chemical reactions that are involved. In this paper we model sugar cataract development as a stochastic hybrid system. Based on this model, we present a probabilistic verification method for computing the probability of sugar cataract formation for different chemical concentrations. Our analysis can potentially provide useful insights into the complicated dynamics of the process and assist in focusing experiments on specific regions of concentrations. The verification method employs dynamic programming based on a discretization of the state space and therefore suffers from the curse of dimensionality. To verify the sugar cataract development process we have developed a parallel dynamic programming implementation that can handle large systems. Although scalability is a limiting factor, this work demonstrates that the technique is feasible for realistic biochemical systems.

New Insights on Stochastic Reachability

by Manuela L. Bujorianu, John Lygeros
"... Abstract—In this paper, we give new characterizations of the stochastic reachability problem for stochastic hybrid systems in the language of different theories that can be employed in studying stochastic processes (Markov processes, potential theory, optimal control). These characterizations are fu ..."
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Abstract—In this paper, we give new characterizations of the stochastic reachability problem for stochastic hybrid systems in the language of different theories that can be employed in studying stochastic processes (Markov processes, potential theory, optimal control). These characterizations are further used to obtain the probabilities involved in the context of stochastic reachability as viscosity solutions of some variational inequalities.
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...ic Hybrid Systems Different modelling paradigms for SHS have been proposed in literature [20], [2]. Applications of SHS range from air traffic management [23], biology [18], to communication networks =-=[19]-=-. Formally, a stochastic hybrid automaton (SHA) is defined as a tuple H = (Q,X , F,R, λ) has been defined in [8], [7]. The executions of an SHA,H , can be described as follows: start with an initial p...

Probabilistic reachability for stochastic hybrid systems: Theory, computations, and applications

by Alessandro Abate , 2007
"... Copyright c © 2007 by Alessandro Abate Probabilistic Reachability for Stochastic Hybrid Systems: ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
Copyright c © 2007 by Alessandro Abate Probabilistic Reachability for Stochastic Hybrid Systems:

Discrete Time Stochastic Hybrid Dynamical Games: Verification & Controller Synthesis

by Maryam Kamgarpour, Jerry Ding, Sean Summers, Alessandro Abate, John Lygeros, Claire Tomlin
"... This paper presents a framework for analyzing probabilistic safety and reachability problems for discrete time stochastic hybrid systems in scenarios where system dynamics are affected by rational competing agents. In particular, we consider a zero-sum game formulation of the probabilistic reach-avo ..."
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This paper presents a framework for analyzing probabilistic safety and reachability problems for discrete time stochastic hybrid systems in scenarios where system dynamics are affected by rational competing agents. In particular, we consider a zero-sum game formulation of the probabilistic reach-avoid problem, in which the control objective is to maximize the probability of reaching a desired subset of the hybrid state space, while avoiding an unsafe set, subject to the worst-case behavior of a rational adversary. Theoretical results are provided on a dynamic programming algorithm for computing the maximal reach-avoid probability under the worst-case adversary strategy, as well as the existence of a maxmin control policy which achieves this probability. The modeling framework and computational algorithm are demonstrated using an example derived from a robust motion planning application.

Robust stability of the new general 2D model of a class of continuous-discrete linear systems,

by M Busłowicz - Bull. Pol. Acad. Sci. Techn., , 2010
"... Abstract. The problems of asymptotic stability and robust stability of the new general 2D model of scalar linear dynamic continuous-discrete systems, standard and positive, are considered. Simple analytic conditions for asymptotic stability and for robust stability are given. These conditions are e ..."
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Abstract. The problems of asymptotic stability and robust stability of the new general 2D model of scalar linear dynamic continuous-discrete systems, standard and positive, are considered. Simple analytic conditions for asymptotic stability and for robust stability are given. These conditions are expressed in terms of coefficients of the model. The considerations are illustrated by numerical examples. The methods proposed can be generalized to scalar Fornasini-Marchesini and Roesser models of 2D continuous-discrete systems.
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...) Example 4. Consider the general uncertain model (1) with a12 ∈ [−5, −1], a21 ∈ [2, 4], a22 ∈ [−0.6, 0.6]. Find values of the coefficient a11 for which the model is robustly stable. In this case from (29) we have α−12 = −20, α + 12 = −2. Because α+12 < 0 and (30) holds, we apply condition (41) of Lemma 4. From this condition we have that the model is robustly stable if and only if a+11 < α − 12/(a − 22+1) = −20/0.4 = −50. The same result one obtains from Algorithm 1. Example 5. Find values of the coefficient a11 for which is robustly stable the positive uncertain general model (1) with a12 ∈ [1, 4], a21 ∈ [2, 6] and a22 ∈ [0, 0.5]. From (29) and Theorem 9 we obtain α− 12 = 2, α+ 12 = 24 and a+11 < −24/0.5 = −48. This means that the positive model is robustly stable if and only if a11 ∈ (−∞, −48). The same result one obtains from Algorithm 1. 4. Concluding remarks Simple analytical conditions for stability and for robust stability of the new general 2D model (1) of scalar continuousdiscrete linear systems, standard and positive, have been given. These conditions are expressed in terms of the coefficients of the model. In particular it has been shown that: • the general model (1) is asymp...

Communication Logic Design and Analysis for Networked Control Systems

by Yonggang Xu, João P. Hespanha
"... Summary. This chapter addresses the control of spatially distributed processes via communication networks with a fixed delay. A distributed architecture is utilized in which multiple local controllers coordinate their efforts through a data network that allows information exchange. We focus our work ..."
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Summary. This chapter addresses the control of spatially distributed processes via communication networks with a fixed delay. A distributed architecture is utilized in which multiple local controllers coordinate their efforts through a data network that allows information exchange. We focus our work on linear time invariant processes disturbed by Gaussian white noise and propose several logics to determine when the local controllers should communicate. Necessary conditions are given under which these logics guarantee boundedness and the trade-off is investigated between the amount of information exchanged and the performance achieved. The theoretical results are validated through Monte Carlo simulations. The resulting closed-loop systems evolve according to stochastic differential equations with resets triggered by stochastic counters. This type of stochastic hybrid system is interesting on its own. 1
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...diffusion process e is defined by (LV )(e) := lim t→s E � V (e(t)|e(s) = e � − V (e) , ∀e ∈ R t − s n , t > s ≥ 0, (21) where E � V (e(t)|e(s) = e � denotes the expectation of V (e(t)) given e(s) = e =-=[15, 5]-=-. The generator for the jump diffusion process described by (18) is given by LV (e) = ∂V (e) ∂V (e) ∂e · Ae + 1 2 tr � σ ′ ∂2V (e) σ ∂e2 � � + λ(e) � � V (ζ) dµ(ζ) − V (e) , (22) where ∂e ∂e2 denote t...

STABILIZING RANDOMLY SWITCHED SYSTEMS

by Debasish Chatterjee, Daniel Liberzon , 2008
"... This article is concerned with stability analysis and stabilization of randomly switched systems under a class of switching signals. The switching signal is modeled as a jump stochastic (not necessarily Markovian) process independent of the system state; it selects, at each instant of time, the ac ..."
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This article is concerned with stability analysis and stabilization of randomly switched systems under a class of switching signals. The switching signal is modeled as a jump stochastic (not necessarily Markovian) process independent of the system state; it selects, at each instant of time, the active subsystem from a family of systems. Sufficient conditions for stochastic stability (almost sure, in the mean, and in probability) of the switched system are established when the subsystems do not possess control inputs, and not every subsystem is required to be stable. These conditions are employed to design stabilizing feedback controllers when the subsystems are affine in control. The analysis is carried out with the aid of multiple Lyapunov-like functions, and the analysis results together with universal formulae for feedback stabilization of nonlinear systems constitute our primary tools for control design.
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...analysis, and arrive at the results directly by employing what we think are less involved and more intuitively appealing techniques. Existing work on stability of stochastic switched systems includes =-=[23, 28, 27, 5, 3, 9, 11, 15, 14, 1]-=-; see also [6, Chapter 1] for a survey techniques employed in these articles. Analysis results obtained via our approach, including those reported in our earlier article [7] where each subsystem was r...

COMPUTER METHODS FOR STABILITY ANALYSIS OF THE ROESSER TYPE MODEL OF 2D CONTINUOUS–DISCRETE LINEAR SYSTEMS

by Mikołaj Busłowicz, Andrzej Ruszewski
"... Asymptotic stability of models of 2D continuous-discrete linear systems is considered. Computer methods for investigation of the asymptotic stability of the Roesser type model are given. The methods require computation of eigenvalue-loci of complex matrices or evaluation of complex functions. The ef ..."
Abstract - Cited by 2 (1 self) - Add to MetaCart
Asymptotic stability of models of 2D continuous-discrete linear systems is considered. Computer methods for investigation of the asymptotic stability of the Roesser type model are given. The methods require computation of eigenvalue-loci of complex matrices or evaluation of complex functions. The effectiveness of the stability tests is demonstrated on numerical examples.

Polynomial stochastic hybrid systems (extended version

by João Pedro Hespanha , 2004
"... Abstract. This paper deals with polynomial stochastic hybrid systems (pSHSs), which generally correspond to stochastic hybrid systems with polynomial continuous vector fields, reset maps, and transition intensities. For pSHSs, the dynamics of the statistical moments of the continuous states evolve a ..."
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Abstract. This paper deals with polynomial stochastic hybrid systems (pSHSs), which generally correspond to stochastic hybrid systems with polynomial continuous vector fields, reset maps, and transition intensities. For pSHSs, the dynamics of the statistical moments of the continuous states evolve according to infinitedimensional linear ordinary differential equations (ODEs). We show that these ODEs can be approximated by finite-dimensional nonlinear ODEs with arbitrary precision. Based on this result, we provide a procedure to build this type of approximations for certain classes of pSHSs. We apply this procedure for several examples of pSHSs and evaluate the accuracy of the results obtained through comparisons with Monte Carlo simulations. These examples include: the modeling of TCP congestion control both for long-lived and on-off flows; state-estimation for networked control systems; and the stochastic modeling of chemical reactions. 1
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...e Markov chains. However, the rate at which these transitions occur may depend on the continuous-state. The model used here for SHSs, whose formal definition can be found in Sec. 2, was introduced in =-=[1]-=- and is heavily inspired by the Piecewise-Deterministic Markov Processes (PDPs) in [2]. Alternative models can be found in [3,4,5]. The extended generator of a stochastic hybrid system allows one to c...

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