110,459 research outputs found
Approximating Non-discrete P Systems
The main goal of this paper is to propose some geometric
approaches to the computations of non-discrete P systems. The behavior
of this kind of P systems is similar to that of classic systems, with
the difference that the contents of the membranes are represented by
non-discrete multisets (the multiplicities can be non-integers) and, consequently,
also the number of applications of a rule in a transition step
can be non-integer.Ministerio de Ciencia y TecnologÃa TIC2002-04220-C03-0
Discrete Data Assimilation in the Lorenz and 2D Navier--Stokes Equations
Consider a continuous dynamical system for which partial information about
its current state is observed at a sequence of discrete times. Discrete data
assimilation inserts these observational measurements of the reference
dynamical system into an approximate solution by means of an impulsive forcing.
In this way the approximating solution is coupled to the reference solution at
a discrete sequence of points in time. This paper studies discrete data
assimilation for the Lorenz equations and the incompressible two-dimensional
Navier--Stokes equations. In both cases we obtain bounds on the time interval h
between subsequent observations which guarantee the convergence of the
approximating solution obtained by discrete data assimilation to the reference
solution
Practical stability with respect to model mismatch of approximate discrete-time output feedback control
This paper establishes a practical stability result for discrete-time output feedback control involving mismatch between the exact system to be stabilised and the approximating system used to design the controller. The practical stability is in the sense of an asymptotic bound on the amount of error bias introduced by the model approximation, and is established using local consistency properties of the systems. Importantly, the practical stability established here does not require the approximating system to be of the same model type as the exact system. Examples are presented to illustrate the nature of our practical stability result
Distribution-based bisimulation for labelled Markov processes
In this paper we propose a (sub)distribution-based bisimulation for labelled
Markov processes and compare it with earlier definitions of state and event
bisimulation, which both only compare states. In contrast to those state-based
bisimulations, our distribution bisimulation is weaker, but corresponds more
closely to linear properties. We construct a logic and a metric to describe our
distribution bisimulation and discuss linearity, continuity and compositional
properties.Comment: Accepted by FORMATS 201
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