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Fixed Points and Stability in Neutral Stochastic Differential Equations with Variable Delays
© Meng Wu et al. 2008
- Received: 4 April 2008
- Accepted: 9 June 2008
- Published: 16 June 2008
We consider the mean square asymptotic stability of a generalized linear neutral stochastic differential equation with variable delays by using the fixed point theory. An asymptotic mean square stability theorem with a necessary and sufficient condition is proved, which improves and generalizes some results due to Burton, Zhang and Luo. Two examples are also given to illustrate our results.
- Banach Space
- Asymptotic Stability
- Stochastic Differential Equation
- Inverse Function
- Contraction Mapping
Liapunov's direct method has been successfully used to investigate stability properties of a wide variety of differential equations. However, there are many difficulties encountered in the study of stability by means of Liapunov's direct method. Recently, Burton [1–4], Jung , Luo , and Zhang  studied the stability by using the fixed point theory which solved the difficulties encountered in the study of stability by means of Liapunov's direct method.
Up till now, the fixed point theory is almost used to deal with the stability for deterministic differential equations, not for stochastic differential equations. Very recently, Luo  studied the mean square asymptotic stability for a class of linear scalar neutral stochastic differential equations. For more details of the stability concerned with the stochastic differential equations, we refer to [8, 9] and the references therein.
Motivated by previous papers, in this paper, we consider the mean square asymptotic stability of a generalized linear neutral stochastic differential equation with variable delays by using the fixed point theory. An asymptotic mean square stability theorem with a necessary and sufficient condition is proved. Two examples is also given to illustrate our results. The results presented in this paper improve and generalize the main results in [1, 6, 7].
Let be a complete filtered probability space and let denote a one-dimensional standard Brownian motion defined on such that is the natural filtration of . Let and with and as . Here denotes the set of all continuous functions with the supremum norm .
and proved the following theorem.
Theorem A (Burton ).
and obtained the following theorem.
Theorem B (Zhang ).
and obtained the following theorem.
Theorem C (Luo ).
Our aim here is to generalize Theorems B and C to (2.7).
which contradicts (2.29). Therefore, (2.11) is necessary for Theorem 2.1. This completes the proof.
Theorem 2.1 improves Theorem C under different conditions.
In this section, we give two examples to illustrate applications of Theorem 2.1 and Corollary 2.4.
Then the zero solution of (3.1) is mean square asymptotically stable.
Then the zero solution of (3.3) is asymptotically stable.
This work was supported by the National Natural Science Foundation of China (10671135) and Specialized Research Fund for the Doctoral Program of Higher Education (20060610005).
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