This paper addresses the robust filtering problem for a class of discrete time-varying Markovian jump systems with randomly occurring nonlinearities and sensor saturation. Two kinds of transition probability matrices for the Markovian process are considered, namely, the one with polytopic uncertainties and the one with partially unknown entries. The nonlinear disturbances are assumed to occur r...

In this paper we extend a performance measure sensitive Markovian bisimulation congruence based on yield and bonus rewards that has been previously defined in the literature, in order to aggregate more states and transitions while preserving compositionality and the values of the performance measures. The extension is twofold. First, we show how to define a performance measure sensitive Markovi...

We present a novel stochastic process algebra that allows the expression of models representing systems comprised of populations of agents distributed over space, where the relative positions of agents influence their interaction. This language, PALOMA, is given both discrete and continuous semantics and it captures multi-class, multi-message Markovian agent models (MMAM). Here we present the d...

We present simpli0ed analytical results for the numerical evaluation of failure time probabilities for a single-unit system whose cumulative wear over time depends on its external environment. The failure time distribution is derived as a one-dimensional Laplace–Stieltjes transform with respect to the temporal variable using a direct solution approach and by inverting an existing two-dimensiona...

Reservoir Computing (RC) is a relatively recent architecture for using recurrent neural networks. It has shown interesting performance in a wide range of tasks despite the simple training rules. We use it here in a logistic regression (LogR) framework. Considering non-Markovian time series with a hidden variable, we show that RC can be used to estimate the transition probabilities at each time ...

In this paper, we deal with the so-called Markovian Arrival process (MAP). An MAP is thought of as a partially observed Markov process, so that the ExpectationMaximization (EM) algorithm is a natural way to estimate its parameters. Then, non-linear filters of basic statistics related to the MAP must be computed. The forward-backward principle is the basic way to do it. Here, bearing in mind a f...

The DAML-based Web Service ontology is one of the major standards for modeling and description of distributed service composition. To analyze the performance of composite service processes specified in DAML-S gives the way to tell whether the process meet the performance requirements, and to choose the process with higher efficiency from those with similar function. In this paper, we propose a ...

We provide a simple interpretation of non-Gaussian nature of the light scattering-intensity fluctuations from an aging colloidal suspension of Laponite using the multiplicative cascade model, Markovian method, and volatility correlations. The cascade model and Markovian method enable us to reproduce most of recent empirical findings: long-range volatility correlations and non-Gaussian statistic...