
The author studies the stochastic Itô system \[ dx=A(t)x dt+\sum_{i=1}^{m}B_i(t)x dW_i(t).\tag{1} \] Here, \(A(t),B_1(t),\ldots,B_m(t)\) are deterministic matrices defined on the semi-axis \(t\geq 0\) and \(W_1(t),\ldots W_m(t)\) are totally independent scalar Wiener processes determined on a complete probability space \((\Omega,F,\text{P})\). These conditions provide the existence and uniqueness of a strong solution \(x(t,x_0)\) satisfying the initial condition \(x(0,x_0)=x_0\). Generalizing the well known result from the theory of ODEs [see \textit{Yu. A. Mitropol'skij, A. M. Samojlenko} and \textit{V. L. Kulik}, Studies in dichotomy of linear systems of differential equations by means of Lyapunov functions. Kiev: Naukova dumka (1990; Zbl 0776.34041)], the author proves that system (1) is exponentially dichotomic in mean square once there exists a symmetric bounded and differentiable matrix \(S(t)\) on \([0,\infty)\) satisfying the condition \[ \dot S(t)+A^T(t)S(t)+S(t)A(t)+ \sum_{i=1}^{m}B_i(t)S(t)B_i(t)0\), where the random variable \(Q(\omega)\) is bounded with probability 1.
Dichotomy, trichotomy of solutions to ordinary differential equations, generating operator, Lyapunov function, Ordinary differential equations and systems with randomness, Stochastic ordinary differential equations (aspects of stochastic analysis), Itô's system, Wiener process, exponential dichotomy, Markov process, Stochastic stability in control theory
Dichotomy, trichotomy of solutions to ordinary differential equations, generating operator, Lyapunov function, Ordinary differential equations and systems with randomness, Stochastic ordinary differential equations (aspects of stochastic analysis), Itô's system, Wiener process, exponential dichotomy, Markov process, Stochastic stability in control theory
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