We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
Attractor systems are useful in neurodynamics,mainly in the modelingof associative memory. Thispaper presentsa complexity theory for continuous phase space dynamical systems with ...
In this paper we study the issue of progress for distributed timed systems modeled as the parallel composition of timed automata. We clarify the requirements of discrete progress (...
When designing computer systems, simulation tools are used to imitate a real or proposed system. Complex, dynamic systems can be simulated without the cost and time constraints in...
— Timed Continuous Petri Net (TCPN) systems are piecewise linear models with input constraints that can approximate the dynamical behavior of a class of timed discrete event syst...