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Hindawi Publishing Corporation Fixed Point Theory and Applications Volume 2008, Article ID 407352, 11 pages doi:10.1155/2008/407352 ResearchArticleFixedPointsandStabilityinNeutralStochasticDifferentialEquationswithVariable Delays Meng Wu, 1 Nan-jing Huang, 1 and Chang-Wen Zhao 2 1 Department of Mathematics, Sichuan University, Chengdu, Sichuan 610064, China 2 College of Business and Management, Sichuan University, Chengdu, Sichuan 610064, China Correspondence should be addressed to Nan-jing Huang, nanjinghuang@hotmail.com Received 4 April 2008; Accepted 9 June 2008 Recommended by Tomas Dom ´ ınguez Benavides We consider the mean square asymptotic stability of a generalized linear neutralstochastic differential equation withvariable 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. Copyright q 2008 Meng Wu et a l. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction 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,Jung5, Luo 6,andZhang7 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 6 studied the mean square asymptotic stability for a class of linear scalar neutralstochastic 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 neutralstochastic differential equation withvariable delays by using the fixed point theory. An asymptotic mean square stability theorem with a necessary 2 Fixed Point Theory and Applications 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. 2. Main results Let Ω, F, {F t } t≥0 ,P be a complete filtered probability space and let Wt denote a one- dimensional standard Brownian motion defined on Ω, F, {F t } t≥0 ,P such that {F t } t≥0 is the natural filtration of Wt.Letat,bt, bt,ct,et,qt ∈ CR ,R, and τt,δt ∈ CR ,R with t − τt →∞and t − δt →∞as t →∞.HereCS 1 ,S 2 denotes the set of all continuous functions φ : S 1 →S 2 with the supremum norm ·. In 2003, Burton 1 studied the equation x t−btx t − τt 2.1 and proved the following theorem. Theorem A Burton 1. Suppose that τtr and there exists a constant α<1 such that t t−r bs r ds t 0 bs r e − t s burdu s s−r bu r du ds ≤ α 2.2 for all t ≥ 0 and ∞ 0 bsds ∞. Then, for every continuous initial function φ : −r, 0 →R,the solution xtxt, 0,φ of 2.1 is bounded and tends to zero as t →∞. Recently, Zhang 7 studied the generalization of 2.1 as follows: x t− n j1 b j tx t − τ j t 2.3 and obtained the following theorem. Theorem B Zhang 7. Suppose that τ j is differential, the inverse function g j t of t − τ j t exists, and there exists a constant α ∈ 0, 1 such that for t ≥ 0, lim inf t→∞ t 0 Qsds > −∞ and n j1 t t−τ j t b j g j s ds t 0 e − t s Qudu b j s τ j s ds t 0 e − t s Qudu Qs s s−τ j s b j g j v dv ds ≤ α, 2.4 where Qt n j1 b j g j t. Then the zero solution of 2.3 is asymptotically stable if and only if t 0 Qsds →∞,ast →∞. Very recently, Luo 6 considered the following neutralstochastic differential equation: d xt − qtx t − τt atxtbtx t − τt dt ctxtetx t − δt dWt 2.5 and obtained the following theorem. Meng Wu et al. 3 Theorem C Luo 6. Let τt be derivable. Assume that there exists a constant α ∈ 0, 1 and a continuous function ht : 0, ∞ → R such that for t ≥ 0, lim inf t→∞ t 0 hsds > −∞ and qt t t−τt ashs ds t 0 e − t s hudu a s−τs h s−τs 1−τ s bs−qshs ds t 0 e − t s hudu hs s s−τs auhu du ds t 0 e −2 t s hudu cs es 2 ds 1/2 ≤ α. 2.6 Then the zero solution of 2.5 is mean square asymptotically stable if and only if t 0 hsds →∞, as t →∞. Now, we consider the generalization of 2.5: d xt − n j1 q j tx t − τ j t n j1 b j tx t − τ j t dt n j1 c j tx t − δ j t dWt, 2.7 with the initial condition xsφs for s ∈ mt 0 ,t 0 , 2.8 where φ ∈ Cmt 0 ,t 0 ,R, b j t,c j t,q j t ∈ CR ,R, τ j t,δ j t ∈ CR ,R , t − τ j t →∞, and t − δ j t →∞as t →∞and for each t 0 ≥ 0, m j t 0 min inf s − τ j s,s≥ t 0 , inf s − δ j s,s≥ t 0 , m t 0 min m j t 0 , 1 ≤ j ≤ n . 2.9 Note that 2.7 becomes 2.5 for n 2, τ 1 t0,τ 2 tτt, b 1 tat,b 2 tbt, q 1 t 0,q 2 tqt, δ 1 t0,δ 2 tδt, c 1 tct, and c 2 tet. Thus, we know that 2.7 includes 2.1, 2.3,and2.5 as special cases. Our aim here is to generalize Theorems B and C to 2.7. Theorem 2.1. Suppose that τ j is differential, and there exist continuous functions h j t : 0, ∞ →R for j 1 ···n and a constant α ∈ 0, 1 such that for t ≥ 0 i lim inf t→∞ t 0 Hsds > −∞, ii n j1 q j t n j1 t t−τ j t h j s ds n j1 t 0 e − t s Hudu h j s − τ j s 1 − τ j s b j s−q j sHs ds n j1 t 0 e − t s Hudu Hs s s−τ j s h j u du ds 2 ⎛ ⎝ t 0 e −2 t s Hudu n j1 c j s 2 ds ⎞ ⎠ 1/2 ≤ α<1, 2.10 where Ht n j1 h j t. 4 Fixed Point Theory and Applications Then the zero solution of 2.7 is mean square asymptotically stable if and only if t 0 Hsds −→ ∞ as t −→ ∞. 2.11 Proof. For each t 0 ,denotebyS the Banach space of all F-adapted processes ψt, ω : mt 0 , ∞× Ω → R which are almost surely continuous in t with norm ψ S E sup s≥mt 0 ψs, ω 2 1/2 . 2.12 Moreover, we set ψt, ωφt for t ∈ mt 0 ,t 0 and E|ψt, ω| 2 →0, as t →∞. At first, we suppose that 2.11 holds. Define an operator P : S →S by Pxtφt for t ∈ mt 0 ,t 0 and for t ≥ t 0 , Pxt φt 0 − n j1 q j t 0 φ t 0 − τ j t 0 − n j1 t 0 t 0 −τ j t 0 h j sφsds e − t t 0 Hudu n j1 q j tx t − τ j t n j1 t t−τ j t h j sxsds t t 0 e − t s Hudu n j1 h j s − τ j s 1 − τ j s b j s − q j sHs x s − τ j s ds − t t 0 e − t s Hudu Hs n j1 s s−τ j s h j uxudu ds t t 0 e − t s Hudu n j1 c j sx s − δ j s dWs : 5 i1 I i t. 2.13 Now, we show the mean square continuity of P on t 0 , ∞.Letx ∈ S, T 1 > 0, and let |r| be sufficiently small. Then E Px T 1 r − Px T 1 2 ≤ 5 5 i1 E I i T 1 r − I i T 1 2 . 2.14 It is easy to verify that E I i T 1 r − I i T 1 2 −→ 0, as r −→ 0,i 1, 2, 3, 4. 2.15 Meng Wu et al. 5 It follows from the last term I 5 in 2.13 that E I 5 T 1 r − I 5 T 1 2 E T 1 t 0 e − T 1 s Hudu e − T 1 r T 1 Hudu − 1 n j1 c j sx s − δ j s dWs T 1 r T 1 e − T 1 r s Hudu n j1 c j sx s − δ j s dWs 2 ≤ 2E T 1 t 0 e −2 T 1 s Hudu e − T 1 r T 1 Hudu − 1 2 n j1 c j s · x s − δ j s 2 ds 2E T 1 r T 1 e −2 T 1 r s Hudu n j1 c j s · x s − δ j s 2 ds −→ 0, as r −→ 0. 2.16 Therefore, P is mean square continuous on t 0 , ∞. Next, we verify that Px ∈ S. Since E|xt|→0, t −δ j t →∞as t →∞,foreach>0, there exists a T 1 >t 0 such that s ≥ T 1 implies E|xs| 2 <and E|xs − δ j s| 2 <. Thus, for t ≥ T 1 , the last term I 5 in 2.13 satisfies E I 5 t 2 ≤ E T 1 t 0 e −2 t s Hudu n j1 c j sx s − δ j s 2 ds E t T 1 e −2 t s Hudu n j1 c j sx s − δ j s 2 ds ≤ E sup s≥mt 0 xs 2 T 1 t 0 e −2 t s Hudu n j1 c j s 2 ds t T 1 e −2 t s Hudu n j1 c j s 2 ds. 2.17 By condition ii and 2.11, there exists T 2 >T 1 such that t ≥ T 2 implies E|I 5 t| 2 < α. 2.18 Thus, E|I 5 t| 2 →0, as t →∞. Similarly, we can show that E|I i t| 2 →0, i 1, 2, 3, 4, as t →∞. Thus, E|Pxt| 2 →0ast →∞. This yields Px ∈ S. Now we show that P : S →S is a contraction mapping. From ii, we can choose ε>0 such that α 2 ε<1. Thus, for each t 0 ≥ 0, we can find a constant L>0 such that 1 1 L n j1 q j t n j1 t t 0 e − t s Hudu Hs s s−τ j s h j u du ds n j1 t t−τ j t h j s ds n j1 t t 0 e − t s Hudu h j s−τ j s1−τ j s b j s−q j sHs ds 2 41 L t t 0 e −2 t s Hudu n j1 c j s 2 ds ≤ α 2 ε<1. 2.19 6 Fixed Point Theory and Applications For any x, y ∈ S, it follows from 2.13, conditions i and ii, and Doob’s L p -inequality see 10 that e sup s≥mt 0 pxs − pys 2 e sup s≥t 0 n j1 q j s x s − τ j s − y s − τ j s n j1 s s−τ j s h j v xv − yv dv s t 0 e − s v hudu n j1 h j v − τ j v 1 − τ j v b j v − q j vhv × x v − τ j v − y v − τ j v dv − s t 0 e − s v hudu hv n j1 v v−τ j v h j u xu − yu du dv s t 0 e − s v hudu n j1 c j v x v − δ j v − y v − δ j v dwv 2 ≤ 1 1 l e sup s≥t 0 n j1 q j s · x s − τ j s − y s − τ j s n j1 s s−τ j s h j v · xv − yv dv s t 0 e − s v hudu n j1 h j v − τ j v 1 − τ j v b j v − q j vhv · x v − τ j v − y v − τ j v dv s t 0 e − s v hudu hv n j1 v v−τ j v h j u · xu − yu du dv 2 41 l sup s≥t 0 e s t 0 e − s v hudu n j1 c j v · x v − δ j v − y v − δ j v 2 dv ≤ e sup s≥mt 0 xs − ys 2 ·sup s≥t 0 1 1 l n j1 q j s n j1 s t 0 e − s v hudu hv v v−τ j v h j u du ds n j1 s s−τ j s h j v dv n j1 s t 0 e − s v hudu × h j v − τ j v 1 − τ j v b j v − q j vhv dv 2 41 l s t 0 e −2 s v hudu n j1 c j v 2 dv ≤ α 2 ε e sup s≥mt 0 xs − ys 2 . 2.20 Meng Wu et al. 7 Therefore, P is contraction mapping with contraction constant α 2 ε. By the contraction mapping principle, P has a fixed point x ∈ S, which is a solution of 2.7 with xsφs on mt 0 ,t 0 and E|xt| 2 →0ast →∞. To obtain the mean square asymptotic stability, we need to show that the zero solution of 2.7 is mean square stable. Let >0 be given and choose δ>0andδ<satisfying the following condition: 4δK 2 1 Le 2 t 0 0 Hudu α 2 ε <, 2.21 where K sup t≥0 {e − t 0 Hsds }.Ifxtxt, t 0 ,φ is a solution of 2.7 with φ 2 <δ,then xtPxt defined in 2.13. We assume that E|xt| 2 <for all t ≥ t 0 . Notice that E|xt| 2 φ 2 <for t ∈ mt 0 ,t 0 .Ifthereexistst ∗ >t 0 such that E|xt ∗ | 2 and E|xt| 2 <for t ∈ mt 0 ,t ∗ ,then2.13 and 2.19 imply that E x t ∗ 2 ≤ 1 Lφ 2 1 n j1 q j t 0 n j1 t 0 t 0 −τ j t 0 h j s ds 2 e −2 t ∗ t 0 Hudu 1 1 L n j1 q j t ∗ n j1 t ∗ t ∗ −τ j t ∗ h j s ds t ∗ t 0 e − t ∗ s Hudu n j1 s s−τ j s h j u du Hs ds t ∗ t 0 e − t ∗ s Hudu n j1 h j s − τ j s 1 − τ j s b j s − q j sHs ds 2 t ∗ t 0 e −2 t ∗ s Hudu n j1 c j s 2 ds ≤ 1 Lδ 1 n j1 q j t 0 n j1 t 0 t 0 −τ j t 0 h j s ds 2 e −2 t ∗ t 0 Hudu α 2 ε <, 2.22 which contradicts the definition of t ∗ . Thus, the zero solution of 2.7 is stable. It follows that the zero solution of 2.7 is mean square asymptotically stable if 2.11 holds. Conversely, we suppose that 2.11 fails. From i, there exists a sequence {t n } with t n →∞as n →∞such that lim n→∞ t n 0 Hudu β,whereβ ∈ R. Then, we can choose a constant J>0 satisfying t n 0 Hudu ∈ −J, J for all n ≥ 1. Denote ωs n j1 h j s − τ j s 1 − τ j s b j s − q j sHs Hs s s−τ j s h j u du 2.23 for all s ≥ 0. From ii,wehave t n 0 e − t n s Hudu ωsds ≤ α, 2.24 8 Fixed Point Theory and Applications which implies t n 0 e s 0 Hudu ωsds ≤ αe t n 0 Hudu ≤ e J . 2.25 Therefore, the sequence { t n 0 e s 0 Hudu ωsds} has a convergent subsequence. Without loss of generality, we can assume that lim n→∞ t n 0 e s 0 Hudu ωsds γ 2.26 for some γ>0. Let k be an integer such that t n t k e s 0 Hudu ωsds < δ 0 8K 2.27 for all n ≥ k,whereδ 0 > 0satisfies8δ 0 K 2 e 2J α 2 ε < 1. Now we consider the solution xtxt, t k ,φ of 2.7 with φt k 2 δ 0 and φs 2 < δ 0 for s<t k . By the similar method in 2.22,wehaveE|xt| 2 < 1fort ≥ t k . We may choose φ so that Gt k : φt k − n j1 q j t k φ t k − τ j t k − n j1 t k t k −τ j t k h j sφsds ≥ 1 2 δ 0 . 2.28 It follows from 2.13 and 2.28 with xtPxt that for n ≥ k, E xt n − n j1 q j t n x t n − τ j t n − n j1 t n t n −τ j t n h j sxsds 2 ≥ G 2 t k e −2 t n t k Hudu − 2Gt k e − t n t k Hudu t n t k e − t n s Hudu ωsds ≥ δ 0 2 e −2 t n t k Hudu δ 0 2 − 2K t n t k e s 0 Hudu ωsds ≥ δ 2 0 8 e −2J > 0. 2.29 If the zero solution of 2.7 is mean square asymptotic stable, then E|xt| 2 E|xt, t k ,φ| 2 →0ast →0. Since t n − τ j t n →∞, t n − δ j t n →∞ as n →∞ and condition ii and 2.11 hold, E xt n − n j1 q j t n x t n − τ j t n − n j1 t n t n −τ j t n h j sxsds 2 −→ 0, as n −→ ∞, 2.30 which contradicts 2.29. Therefore, 2.11 is necessary for Theorem 2.1. This completes the proof. Remark 2.2. Theorem 2.1 still holds if condition ii is satisfied for t ≥ t a for some t a ∈ R . Meng Wu et al. 9 Remark 2.3. Theorem 2.1 improves Theorem C under different conditions. Corollary 2.4. Suppose that τ j is differential, the inverse function g j t of t − τ j t exists, and there exists a constant α ∈ 0, 1 such that for t ≥ 0, lim inf t→∞ t 0 Qsds > −∞ and n j1 q j t n j1 t t−τ j t b j g j s ds n j1 t 0 e − t s Qudu b j sτ j s − q j sQs ds n j1 t 0 e − t s Qudu Qs s s−τ j s b j g j u du ds2 ⎛ ⎝ t 0 e −2 t s Qudu n j1 c j s 2 ds ⎞ ⎠ 1/2 ≤ α<1, 2.31 where Qt n j1 − b j g j t. Then the zero solution of 2.7 is mean square asymptotically stable if and only if t 0 Qsds →∞as t →∞. Remark 2.5. When h j t−b j g j t for j 1 ···n, Theorem 2.1 reduces to Corollary 2.4.Onthe other hand, we choose q j t ≡ c j t ≡ 0andb j ≡−b j for j 1 ···n,thenCorollary 2.4 reduces to Theorem B. 3. Two examples In this section, we give two examples to illustrate applications of Theorem 2.1 and Corollary 2.4. Example 3.1. Consider the following linear neutralstochastic delay differential equation: d xt− xt − t/2 1000 − xt−t/2 1616t − 3sin t4 4848t x t − t 4 dt xt 24 √ 3 4t − xt−sin t 12 √ 34t dWt. 3.1 Then the zero solution of 3.1 is mean square asymptotically stable. Proof. Choosing h 1 t1/8 16t and h 2 t7/48 64t in Theorem 2.1,wehave Ht 1 8 16t 7 48 64t , 11 48 64t ≤ Ht ≤ 13 48 64t , 2 j1 t t−τ j t h j s ds t t/2 1 8 16s ds t 3t/4 7 48 64s ds −→ 0.07479, as t −→ ∞, 2 j1 t 0 e − t s Hudu Hs s s−τ j s h j u du ds ≤ t 0 e − t s 11/4864udu 13 48 64s ·0.07479 ds ≤ 0.08839, 2 ⎛ ⎝ t 0 e −2 t s Hudu 2 j1 c j s 2 ds ⎞ ⎠ 1/2 ≤ 2 t 0 e − t s 11/2432udu 1 824 32s ds 1/2 ≤ 0.21320, 2 j1 t 0 e − t s Hudu h j s − τ j s1 − τ j s b j s − q j sHs ds ≤ t 0 e − t s 11/4864udu 0.013 48 64s 17 144 192s ds ≤ 0.013 11 17 33 0.51634. 3.2 10 Fixed Point Theory and Applications It easy to check that ∞ 0 Hsds ∞.Letα 0.001 0.07479 0.08839 0.21320 0.51634. Then, α 0.89372 < 1andthezerosolutionof3.1 is mean square asymptotically stable by Theorem 2.1. Example 3.2. Consider the following delay differential equation: x t− 1 6 4t x t − t 3 − 1 12 4t x t − 2 3 t . 3.3 Then the zero solution of 3.3 is asymptotically stable. Proof. Choosing h 1 th 2 t1/4 4t in Theorem 2.1,wehaveHt1/2 2t and 2 j1 t t−τ j t h j s ds t 2/3t 1 4 4s ds t t/3 1 4 4s ds −→ 1 2 ln 3 − 1 4 ln 2 0.37602, as t −→ ∞, 2 j1 t 0 e − t s Hudu Hs s s−τ j s h j u du ds ≤ t 0 e − t s 1/22udu 1 2 2s ·0.37602 ds ≤ 0.37602. 3.4 Notice that q j tc j t ≡ 0and 2 j1 h j s − τ j s 1 − τ j s b j s − q j sHs 3 12 8s · 2 3 − 1 6 4s 3 12 4s · 1 3 − 1 12 4s 0. 3.5 It is easy to see that all the conditions of Theorem 2.1 hold for α 0.376020.37602 0.75204 < 1. Thus, Theorem 2.1 implies that the zero solution of 3.3 is asymptotically stable. However, Theorem B cannot be used to verify that the zero solution of 3.3 is asymptotically stable. In fact, b 1 t1/6 4t, b 2 t1/12 4t, b 1 g 1 t 1/6 6t, b 2 g 2 t 1/12 12t,and|Qt| 1/4 4t.Ast →∞, 2 j1 t t−τ j t b j g j s ds ≤ t 2/3t 1 6 6s ds t t/3 1 12 12s ds −→ 1 4 ln 3 − 1 6 ln 2 0.15913. 3.6 Notice that 2 j1 b j sτ j s − q j sQs 1 18 12s 1 18 6s ≤ 1 4 4s . 3.7 It follows from 3.7 that 2 j1 t 0 e − t s Qudu b j sτ j s − q j sQs ds ≤ t 0 e − t s 1/44udu 1 4 4s ds ≤ 1. 3.8 [...]... 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Hindawi Publishing Corporation Fixed Point Theory and Applications Volume 2008, Article ID 407352, 11 pages doi:10.1155/2008/407352 Research Article Fixed Points and Stability in Neutral Stochastic Differential. point approach to the stability of a Volterra integral equation,” Fixed Point Theory and Applications, vol. 2007, Article ID 57064, 9 pages, 2007. 6 J. Luo, Fixed points and stability of neutral. 3679–3687, 2004. 4 T. A. Burton and T. Furumochi, Fixed points and problems in stability theory for ordinary and functional differential equations, ” Dynamic Systems and Applications, vol. 10, no.