Mean-Square Stability of Uncertain Delayed Stochastic Systems Driven by G-Brownian Motion

Existence and Measurability of the Solution of the Stochastic Differential Equations Driven by Fractional Brownian Motion

New Result concerning Mean Square Exponential Stability of Uncertain Stochastic Delayed Hopfield Neural Networks

By using the Lyapunov functional method, stochastic analysis, and LMI (linear matrix inequality) approach, the mean square exponential stability of an equilibrium solution of uncertain stochastic Hopfield neural networks with delayed is presented. The proposed result generalizes and improves previous work. An illustrative example is also given to demonstrate the effectiveness of the proposed re...

p-Moment stability of solutions to stochastic differential equations driven by G-Brownian motion

Stability theorem for stochastic differential equations driven by G-Brownian motion

In this paper, stability theorems for stochastic differential equations and backward stochastic differential equations driven by G-Brownian motion are obtained. We show the existence and uniqueness of solutions to forward-backward stochastic differential equations driven by G-Brownian motion. Stability theorem for forward-backward stochastic differential equations driven by G-Brownian motion is...

Mean-square filtering for uncertain linear stochastic systems

This paper presents the mean-square joint filtering and parameter identification problem for uncertain linear stochastic systems with unknown parameters in both, state and observation, equations, where the unknown parameters are considered Wiener processes. The original problem is reduced to the filtering problem for an extended state vector that incorporates parameters as additional states. Th...