Recently it was shown [W. Koch, F. Grossmann, J.T. Stockburger, J. Ankerhold, Phys. Rev. Lett. 100 (2008) 230402] that a combination of an exact stochastic decomposition of non-Markovian dissipative quantum dynamics with the time-dependent semiclassical initial value formalism offers a powerful tool to describe quantum Brownian motion in domains of parameter space where other approaches fail. I...

This paper deals with robust exponential stability of Markovian jumping stochastic Cohen–Grossberg neural networks (MJSCGNNs) with mode-dependent probabilistic time-varying delays, continuously distributed delays and impulsive perturbations. By construction of novel Lyapunov–Krasovskii functional having the triple integral terms, the double integral terms having the positive definite matrices d...

This paper investigates the H∞ state estimation problem for gene regulatory networks (GRNs) with timevarying delays and Markovian jumping parameters. The system output, which is used by the estimator, is assumed to be sampled and held during a sampling period. To deal with the time-varying delays, each delay function is limited to certain lower and upper bounds. By using Lyapunov-Krasovskii fun...

In this paper, a receding horizon control strategy for a class of bilinear discrete-time systems with Markovian jumping parameters and constraints is investigated. Specifically, the stochastic jump system under consideration involves control and state multiplicative noise and partly unknown transition probabilities (TPs). The receding horizon formulation adopts an on-line optimization paradigm ...

We propose a generalization of the quantum Markovian equation for observables. In this generalized equation, we use superoperators that are fractional powers of completely dissipative superoperators. We prove that the suggested superoperators are infinitesimal generators of completely positive semigroups and describe the properties of this semigroup. We solve the proposed fractional quantum Mar...

A piecewise-constant conditional intensity model (PCIM) is a non-Markovian model of temporal stochastic dependencies in continuoustime event streams. It allows efficient learning and forecasting given complete trajectories. However, no general inference algorithm has been developed for PCIMs. We propose an effective and efficient auxiliary Gibbs sampler for inference in PCIM, based on the idea ...

We use the stochastic quantization method to obtain the free scalar propagator of a finite temperature field theory formulated in Minkowski spacetime. First we use the Markovian stochastic quantization approach to present the two-point function of the theory. Second, we assume a Langevin equation with a memory kernel and Einstein’s relations with colored noise. The convergence of the stochastic...