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        Pseudo Almost Automorphic Solutions for Non-autonomous Stochastic Di ff erential Equations with Exponential Dichotomy

        2014-03-02 11:52:18

        (School of Mathematics,Jilin University,Changchun,130012)

        Pseudo Almost Automorphic Solutions for Non-autonomous Stochastic Di ff erential Equations with Exponential Dichotomy

        DU JIN-SHI,SUN KAI AND WANG YAN

        (School of Mathematics,Jilin University,Changchun,130012)

        Communicated by Li Yong

        In this paper,we consider the existence and uniqueness of the solutions which are pseudo almost automorphic in distribution for a class of non-autonomous stochastic di ff erential equations in a Hilbert space.In conclusion,we use the Banach contraction mapping principle and exponential dichotomy property to obtain our main results.

        pseudo almost automorphy,exponential dichotomy,non-autonomous stochastic di ff erential equation

        1 Introduction

        Liu and Sun[1]introduced the concept of almost automorphy in distribution and studied the almost automorphy in distribution solutions of stochastic di ff erential equations driven by L′evy noise.Chen and Lin[2]researched the square-mean pseudo almost automorphic process and its applications.

        In this paper,we consider the existence and uniqueness of the solutions which are pseudo almost automorphic in distribution for a class of non-autonomous stochastic di ff erential equations of the form

        whereA(t)is a family of closed linear operators satisfying the Acquistapace-Terrani conditions(see[3–4]),f(t,x),g(t,x)are square-mean pseudo almost automophic int∈R foreachx∈L2(P,H),andf,gare assumed to satisfy Lipschitz conditions with respect tox.

        This paper is organized as follows.In Section 2,we provide de fi nitions,lemmas and propositions.In Section 3,we prove our main result.

        2 Preliminaries

        In this section,we provide some preliminaries.The readers may fi nd more details in[1–9].

        2.1 The Norm of the Space

        Throughout this paper,we assume that(H,‖·‖)is a real separable Hilbert space.Let (?,F,P)be a complete probability space.The notationL2(P,H)stands for the space of allH-valued random variablesxsuch that

        Forx∈L2(P,H),let

        Then it is routine to check thatL2(P,H)is a Hilbert space equipped with the norm‖·‖2. LetW(t)be a two-sided standard one-dimensional Brownian motion de fi ned on the fi ltered probability space(?,F,P,Ft),whereFt=σ{W(u)?W(v);u,v≤t}.

        2.2 Square-mean Pseudo Almost Automorphic

        De fi nition 2.1[1]A stochastic process x:R→L2(P,H)is said to be L2-continuous if for any s∈R,

        Note that if anH-valued process isL2-continuous,then it is necessarily stochastically continuous.

        De fi nition 2.2[2]A stochastic process x:R→L2(P,H)is said to be L2-bounded if there exists an M>0such that

        The collection of allL2-bounded continuous processes is denoted bySBC(R;L2(P,H)).

        De fi nition 2.4[1]An L2-continuous stochastic process x:R→L2(P,H)is said to besquare-mean almost automorphic if every sequence of real numbers{}has a subsequence{sn}such that for some stochastic processes y:R→L2(P,H),andhold for all t∈R.

        The collection of all square-mean almost automorphic stochastic processesx:R→L2(P,H)is denoted byAA(R;L2(P,H)).

        De fi nition 2.5[2]An L2-continuous process f(t):R→L2(P,H)is said to be squaremean pseudo almost automorphic if it can be decomposed as

        The collection of all square-mean pseudo almost automorphic processesf(t):R→L2(P,H)is denoted bySPAA(R;L2(P,H)).

        Lemma 2.1[8]If x,x1and x2are all square-mean almost automorphic stochastic processes, then the following statements hold:

        (1)x1+x2is square-mean almost automorphic;

        (2)λx is square-mean almost automorphic for every scalar λ;

        Lemma 2.2[8]AA(R;L2(P,H))is a Banach space when it is equipped with the norm

        Lemma 2.3[2]SBC0(R;L2(P,H))is a Banach space with the norm‖·‖∞.

        Lemma 2.4[2]SPAA(R;L2(P,H))is a Banach space with the norm‖·‖∞.

        De fi nition 2.6[8]A function f:R×L2(P,H)→L2(P,H),(t,x)(t,x),which is jointly continuous,is said to be square-mean almost automorphic in t∈Rfor eachx∈L2(P,H),if for every sequence of real numbers{}there exists a subsequence{sn}such that for some function?f,andhold for all t∈Rand each x∈L2(P,H).

        Proposition 2.1[8]Let f:R×L2(P,H)→L2(P,H),(t,x)→f(t,x)be square-mean almost automorphic in t∈Rfor each x∈L2(P,H).Assume that f satis fi es Lipschitz condition in the following sense:

        where M>0is independent of t.Then for any square-mean almost automorphic process x:R→L2(P,H),the stochastic process F:R→L2(P,H)given by F(t):=f(t,x(t))is square-mean almost automorphic.

        Proposition 2.2[2]Let f(t,x)∈SPAA(R×L2(P,H),L2(P,H)).Assume that f satis fi es Lipschitz condition in the following sense:

        where M>0is independent of t.Then f(t,x(t))∈SPAA(R;L2(P,H))for any x∈SPAA(R;L2(P,H)).

        2.3 Acquistapace-Terrani Condition and Exponential Dichotomy

        Proposition 2.3[3–5]If the Acquistapace-Terreni condition(ATC)is satis fi ed,that is, there exists a constant K0>0and a set of real numbers α1,α2,···,αk,β1,β2,···,βkwith0≤βi<αi≤2,i=1,2,···,k,such that

        where

        and there exists a constant K1≥0such that

        then there exists a unique evolution family{U(t,s),t≥s>?∞}on L2(P,H).

        We assume that ATC is satis fi ed in the rest of this paper.

        LetBbe a Banach space.We denote byLb(B)the Banach space of all bounded linear operators fromBtoBequipped with natural operator norm.

        De fi nition 2.7[7,10]A family of bounded linear operators{U(t,s):t≥s,t,s∈R}on L2(P,H)associated with A(t)is said to be an evolution family of operators if the following conditions hold:

        (1)U(t,s)U(s,r)=U(t,r)for every t≥s≥r;

        (2)U(t,t)=I for all t∈R;

        (3)(t,s)→U(t,s)∈Lb(L2(P,H))is strongly continuous for every t>s;

        De fi nition 2.8[10]An evolution family{U(t,s):t≥s,t,s∈R}is said to exponential dichotomy(ED),if there are projectors P(t),t∈R,being uniformly bounded and strongly continuous in t and two constants K≥1and ω>0such that

        (1)P(t)U(t,s)=U(t,s)P(s);

        (2)the restriction U(s,t):Q(s)L2(P,H)→Q(t)L2(P,H)of U(t,s)is invertible,and U(t,s)=(U(s,t))?1,t≥s;

        where Q(s)=I?P(s).

        De fi nition 2.9An L2-continuous stochastic process x(t)t∈Ris called a mild solution of the problem(1.1)if it satis fi es the corresponding stochastic integral equation:

        for all t≥a0and for each a0∈R.

        LetP(H)be the space of all Borel probability measures onHendowed with theβmetric:

        wherefis a Lipschitz continuous real-valued function onHwith the norms

        De fi nition 2.10[1]An H-valued stochastic process X(t)is said to be almost automorphic in distribution if its lawμ(t)is a P(H)-valued almost automorphic mapping,i.e.,for everysequence of real numbers{},there exist a subsequence{sn}and a P(H)-valued mapping(t)such that

        hold for all t∈R.

        De fi nition 2.11An L2-continuous process f(t):R→L2(P,H)is said to be pseudo almost automorphic in distribution,if it can be decomposed as f=g+φ,where g is almost automorphic in distribution and φ∈SBC0(R;L2(P,H)).

        2.4 A Variant of Gronwall’s Lemma

        Refering to[6]and[9],we have the following version of Gronwall’s lemma.

        Lemma 2.5Let g:R→Rbe a bounded and continuous function such that for all t∈R,

        where α:R→Ris a bounded function and satis fi es

        β1,···,β2n≥0are constants and δ1,···,δ2n>2β+ξ are constants with

        Let

        Then for every γ∈(0,δ?2β?ξ]we have

        where

        Proof.Let

        Then

        Let

        Then fort∈RS,(2.5)is always true.Let

        Thenν(t)is nonnegative and bounded.Fort∈S,we have

        Therefore,

        i.e.,fort∈S,

        Fort∈RS,byν(t)=0,we have

        Hence,for allt∈R,

        i.e.,there existst0≥0 such that for allt≥t0,

        Then

        Lettingt→+∞,we have

        which is a contradiction.

        and

        Then

        By a proof similar to that of Lemma 3.3 in[6],we have

        Then

        Substituting(2.7)into(2.6)yields

        Since

        we have

        3 Main Result

        3.1 Assumptions

        In order to get our result,we make the following assumptions:

        (H1)The problem(1.1)satis fi es an exponential dichotomy.There exists an invariant projectorP(t)of the evolution familyU(t,s)generated byA(t),and constantsM≥1 andω>0 such that

        and

        moreover,

        (H3)The functionsfandgare square-mean pseudo almost automorphic int∈R for eachx∈L2(P,H),and there exist positive numbersLfandLgsuch that for anyx,y∈L2(P,H),

        The functionsfandgcan be decomposed as

        anda,b,α,βsatisfy Lipschitz condition with respect toxuniformly int,that is,there exist positive numbersLa,Lb,Lα,Lβsuch that

        for allt∈R andx,y∈L2(P,H).

        3.2 Theorem

        Theorem 3.1If(H1)–(H3)are satis fi ed,then(1.1)has a unique L2-bounded mild solution provided that

        Furthermore,this unique L2-bounded solution is pseudo almost automorphic in distribution provided that

        Proof.Note that

        is well de fi ned for eacht∈R and satis fi es(2.3).And if it isL2-bounded,then it is a mild solution of(1.1).Similarly to[1],we divide the proof into three steps.

        Step 1.AnL2-bounded solution is necessarilyL2-continuous.

        It follows from(H1)that there exist constantsM>0 andω>0 such that

        and

        Ifx(t)is anL2-bounded solution,i.e.,(3.3)holds,then it follows from Cauchy-Schwarz inequality and It?o’s isometry that fort≥r,

        From the de fi nition ofU(·,·),we know that for anyx∈H,

        Note that

        by Lebesgue dominated convergence theorem,we have

        Under the conditions of the theorem andX(·)isL2-bounded,by Lemma 2.2 we have

        Similarly to(3.4),we can get

        Hence

        The similar argument yields that

        that is,x(·)isL2-continuous.

        Step 2.The existence and uniqueness of anL2-bounded solution.

        Denote byCb(R;L2(P,H))the Banach space of all boundedL2-continuous mapping from R toL2(P,H)endowed with the norm‖·‖∞.Let us consider the operator?acting onCb(R;L2(P,H))de fi ned by

        According to(H1),(3.4)and(3.5),?x(·)is anL2-bounded process if so isx(·).Then,by the proof of Step 1,?x(·)is anL2-continuous process ifx(·)is anL2-bounded process.It follows that the operator?mapsCb(R;L2(P,H))into itself.For anyx,y∈Cb(R;L2(P,H))and eacht∈R,we prove that?is a contraction mapping onCb(R;L2(P,H)).

        We have

        And in the same way,we have

        By It?o’s isometry property of stochastic integrals,we have

        Similarly,we can get

        So we can conclude that,for allt∈R,

        Let

        Then

        This means that?is a contraction mapping whenL0<1.Therefore,there exists a uniquex(·)such that?x=x,which is the uniqueL2-bounded mild solution to(1.1).

        Step 3.Pseudo almost automorphy of theL2-bounded solution.

        For givenx∈SPAA(R;L2(P,H)),by the de fi nition of the mapping?,we have

        Let us prove that the mappingΓxis almost automorphic in distribution,and that?x∈SBC0(R;L2(P,H)).

        First,by the proof of Step 1,it can be easily proved thatΓx(·)isL2-continuous process ifx(·)is anL2-bounded process.

        By Theorem 2.1 and Proposition 2.1,we havea(t,x(t)),α(t,x(t))∈AA(R;L2(P,H)).Letbe an arbitrary sequence of real numbers.There exist a subsequence{sn}n∈Nofand stochastic processes?a,?αsuch that

        hold for allt∈R.Let?x(·)satisfy the equation

        By the proofs of Steps 1 and 2,(3.7)has a uniqueL2-bounded solution.

        Let

        ThenWnis also a Wiener process with the same law asW.Letσ=r?sn.Then we have

        In the similar way to[6],we have

        It follows thatΓx(t+sn)has the same distribution asxn(t)for eacht∈R andxn(t)is unique andL2-bounded.By(H2),for anyε>0,there exists anN∈N such that for alln>N,we have

        Moreover,we de fi ne

        Then we have

        with

        Note that

        By means of Lemma 2.2,we have

        and consequently,

        Sinceais square-mean almost automorphic int,εis arbitrary and(·)is bounded inL2(P,H),by Lebesgue dominated convergence theorem,we have that

        In a similar way,we can also conclude that

        with

        By It?o’s isometry,we obtain

        with

        Similarly,we can conclude that

        with

        By above estimations,we have

        with

        By Lemma 2.5 and(3.2),it follows thatE‖xn(t)?(t)‖2→0 asn→∞for eacht∈R.

        SinceΓx(t+sn)has the the same distribution asxn(t),it follows thatΓx(t+sn)→(t) in distribution asn→∞.And we can show in the same way that?x(t?sn)→Γx(t)in distribution asn→∞for eacht∈R.

        Then,we prove that?mapsSBC0(R;L2(P,H))into itself.

        By using the arguments above,we know that?xis anL2-continuous process.Sinceb,β∈SBC0(R;L2(P,H)),it can be proved that?xisL2-bounded,i.e.,?x∈SBC(R;L2(P,H)). Hence,we only need to prove

        Note that

        A simple calculation yields that

        Similarly,we have?2→0 asT→∞.On the other hand,

        Similarly,we have?4→0 asT→∞.

        So far,we have veri fi ed

        i.e.,?x∈SBC0(R;L2(P,H)).Therefore,?xis pseudo almost automorphic in distribution.

        Acknowledgement The authors sincerely thank Professor Liu Zhen-xin for his encouragement and helpful discussions.

        [1]Liu Z,Sun K.Almost automorphic solutions for stochastic di ff erential equations driven by Levy noise.J.Funct.Anal.,2014,266:1115–1149.

        [2]Chen Z,Lin W.Square-mean pseudo almost automorphic process and its application to stochastic evolution equations.J.Funct.Anal.,2011,261:69–89.

        [3]Acquistapace P.Evolution operators and strong solution of abstract linear nonautonomous parabolic equations.Di ff erential Integral Equations,1988,1:433–457.

        [4]Acquistapace P,Terreni B.A uni fi ed approach to abstract linear nonautonomous parabolic equations.Rend.Sem.Mat.Univ.Padova,1987,78:47–107.

        [5]Chang Y,Zhao Z,N’Gu′er′ekata G M.Square-mean almost automorphic mild solutions to nonautonomous stochastic di ff erential equations in Hilbert spaces.Comput.Math.Appl.,2011, 61:384–391.

        [6]Kamenskii M,Mellah O,Raynaud De Fitte P.Weak averaging of semilinear stochastic di ff erential equations with almost periodic coe ff cients.arXiv:1210.7412v2[math.PR].Submitted on 28 Oct.2012.

        [7]Bezandry P H,Diagana T.Square-mean almost periodic solutions nonautonomous stochastic di ff erential equations.Electron.J.Di ff erential Equations,2007,117:1–10.

        [8]Fu M,Liu Z.Square-mean almost automorphic solutions for some stochastic di ff erential equations.Proc.Amer.Math.Soc.,2010,138:3689–3701.

        [9]Deng S,Zhang W.Remarks on projected gronwall inequality(in Chinese).J.Math.Res. Exposition,2002,22:307–313.

        [10]Cui J,Yan L.On almost automorphic mild solutions for nonautonomous stochastic evolution equations.Abstr.Appl.Anal.,2012,2012:Article ID 870831,25pp.

        tion:60H25,34C27,34F05,34G20

        A

        1674-5647(2014)02-0139-18

        10.13447/j.1674-5647.2014.02.05

        Received date:Oct.9,2012.

        Foundation item:The Undergraduate Research Training Program Grant(J1030101)and the NSF(11271151) of China.

        E-mail address:djs052611@sina.cn(Du J S).

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