139 research outputs found
Weight Try-Once-Discard Protocol-Based L_2 L_infinity State Estimation for Markovian Jumping Neural Networks with Partially Known Transition Probabilities
It was the L_2 L_infinity performance index that for the first time is
initiated into the discussion on state estimation of delayed MJNNs with with
partially known transition probabilities, which provides a more general
promotion for the estimation error.The WTOD protocol is adopted to dispatch the
sensor nodes so as to effectively alleviate the updating frequency of output
signals. The hybrid effects of the time delays, Markov chain, and protocol
parameters are apparently reflected in the co-designed estimator which can be
solved by a combination of comprehensive matrix inequalities
Asynchronous switching control for fuzzy Markov jump systems with periodically varying delay and its application to electronic circuits
This article focuses on addressing the issue of asynchronous H∞ control for Takagi-Sugeno (T-S) fuzzy Markov jump systems with generally incomplete transition probabilities (TPs). The delay is assumed to vary periodically, resulting in one monotonically increasing interval and one monotonically decreasing interval during each period. Meanwhile, a new Lyapunov-Krasovskii functional (LKF) is devised, which depends on membership functions (MFs) and two looped functions formulated for the monotonic intervals. Since the modes and TPs of the original system are assumed to be unavailable, an asynchronous switching fuzzy controller on the basis of hidden Markov model is proposed to stabilize the fuzzy Markov jump systems (FMJSs) with generally incomplete TPs. Consequently, a stability criterion with improved practicality and reduced conservatism is derived, ensuring the stochastic stability and H∞ performance of the closed-loop system. Finally, this technique is employed to the tunnel diode circuit system, and a comparison example is given, which verifies the practicality and superiority of the method
Stochastic filtering of a pure jump process with predictable jumps and path-dependent local characteristics
The objective of this paper is to study the filtering problem for a system of
partially observable processes , where is a non-Markovian pure-jump
process representing the signal and is a general jump-diffusion which
provides observations. Our model covers the case where both processes are not
necessarily quasi left-continuous, allowing them to jump at predictable
stopping times. By introducing the Markovian version of the signal, we are able
to compute an explicit equation for the filtering process via the innovations
approach
Studying the effects of adding spatiality to a process algebra model
We use NetLogo to create simulations of two models of disease transmission originally expressed in WSCCS. This allows us to introduce spatiality into the models and explore the consequences of having different contact structures among the agents. In previous work, mean field equations were derived from the WSCCS models, giving a description of the aggregate behaviour of the overall population of agents. These results turned out to differ from results obtained by another team using cellular automata models, which differ from process algebra by being inherently spatial. By using NetLogo we are able to explore whether spatiality, and resulting differences in the contact structures in the two kinds of models, are the reason for this different results. Our tentative conclusions, based at this point on informal observations of simulation results, are that space does indeed make a big difference. If space is ignored and individuals are allowed to mix randomly, then the simulations yield results that closely match the mean field equations, and consequently also match the associated global transmission terms (explained below). At the opposite extreme, if individuals can only contact their immediate neighbours, the simulation results are very different from the mean field equations (and also do not match the global transmission terms). These results are not surprising, and are consistent with other cellular automata-based approaches. We found that it was easy and convenient to implement and simulate the WSCCS models within NetLogo, and we recommend this approach to anyone wishing to explore the effects of introducing spatiality into a process algebra model
A modest approach to Markov automata
A duplicate of https://zenodo.org/record/5758839.
Reason: The submitter forgot to indicate the DOI before publishing, so it got another one assigned automatically, which is unchangeable
Scalable Performance Analysis of Massively Parallel Stochastic Systems
The accurate performance analysis of large-scale computer and communication systems is directly
inhibited by an exponential growth in the state-space of the underlying Markovian performance
model. This is particularly true when considering massively-parallel architectures
such as cloud or grid computing infrastructures. Nevertheless, an ability to extract quantitative
performance measures such as passage-time distributions from performance models of
these systems is critical for providers of these services. Indeed, without such an ability, they
remain unable to offer realistic end-to-end service level agreements (SLAs) which they can have
any confidence of honouring. Additionally, this must be possible in a short enough period of
time to allow many different parameter combinations in a complex system to be tested. If we
can achieve this rapid performance analysis goal, it will enable service providers and engineers
to determine the cost-optimal behaviour which satisfies the SLAs.
In this thesis, we develop a scalable performance analysis framework for the grouped PEPA
stochastic process algebra. Our approach is based on the approximation of key model quantities
such as means and variances by tractable systems of ordinary differential equations (ODEs).
Crucially, the size of these systems of ODEs is independent of the number of interacting entities
within the model, making these analysis techniques extremely scalable. The reliability of our
approach is directly supported by convergence results and, in some cases, explicit error bounds.
We focus on extracting passage-time measures from performance models since these are very
commonly the language in which a service level agreement is phrased. We design scalable analysis
techniques which can handle passages defined both in terms of entire component populations
as well as individual or tagged members of a large population.
A precise and straightforward specification of a passage-time service level agreement is as important
to the performance engineering process as its evaluation. This is especially true of
large and complex models of industrial-scale systems. To address this, we introduce the unified
stochastic probe framework. Unified stochastic probes are used to generate a model augmentation
which exposes explicitly the SLA measure of interest to the analysis toolkit. In this thesis,
we deploy these probes to define many detailed and derived performance measures that can
be automatically and directly analysed using rapid ODE techniques. In this way, we tackle
applicable problems at many levels of the performance engineering process: from specification
and model representation to efficient and scalable analysis
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