Hesitant linguistic preference relations (HLPRs) are useful tools for decision makers (DMs) to express their qualitative judgements. However, the traditional HLPRs have one prominent drawback, which is to sort the linguistic values in a hesitant linguistic set. This will distort the DMs' initial judgements. In the present paper, a revised definition of HLPR, called general HLPR (GHLPR), was proposed. A characterization was explored for LPRs. Then, the characterization was extended to GHLPRs. Based on the characterization, the estimation of unknown entries in incomplete GHLPRs were carried out by two algorithms. The group decision-making problems with incomplete GHLPRs were settled by another algorithm. Finally, a case study was illustrated, and comparisons showed that our methods were more reasonable than the existent methods.
Citation: Lei Zhao. Managing incomplete general hesitant linguistic preference relations and their application[J]. AIMS Mathematics, 2024, 9(10): 28870-28894. doi: 10.3934/math.20241401
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Hesitant linguistic preference relations (HLPRs) are useful tools for decision makers (DMs) to express their qualitative judgements. However, the traditional HLPRs have one prominent drawback, which is to sort the linguistic values in a hesitant linguistic set. This will distort the DMs' initial judgements. In the present paper, a revised definition of HLPR, called general HLPR (GHLPR), was proposed. A characterization was explored for LPRs. Then, the characterization was extended to GHLPRs. Based on the characterization, the estimation of unknown entries in incomplete GHLPRs were carried out by two algorithms. The group decision-making problems with incomplete GHLPRs were settled by another algorithm. Finally, a case study was illustrated, and comparisons showed that our methods were more reasonable than the existent methods.
Modern problems of natural science lead to the need to generalize the classical problems of mathematical physics, as well as to the formulation of qualitatively new problems, which include non-local problems for differential equations. Among nonlocal problems, problems with integral conditions are of great interest. Integral conditions are encountered in the study of physical phenomena in the case when the boundary of the process flow region is inaccessible for direct measurements. Inverse problems arise in various fields of human activity, such as seismology, mineral exploration, biology, medical visualization, computed tomography, earth remote sensing, spectral analysis, nondestructive control, etc. Various inverse problems for certain types of partial differential equations have been studied in many works. A more detailed bibliography and a classification of problems are found in [1,2,3,4,5]. Inverse problems for one-dimensional pseudo-parabolic equations of third-order were studied in [6]. The existence and uniqueness of the solution of the inverse problem for the third order pseudoparabolic equation with integral over-determination condition is studied in [7]. Khompysh [8] investigated the reconstruction of unknown coefficient in pseudo-parabolic inverse problem with the integral over determination condition and studied the uniqueness and existence of solution by means of method of successive approximations. Studies of wave propagation in cold plasma and magnetohydrodynamics also reduce to the partial differential equations of fourth-order. To the study of nonlocal boundary value problems (including integral conditions) for partial differential equations of the fourth-order are devoted large number of works, see, for example, [9,10]. It should be noted that boundary value problems with integral conditions are of particular interest. From physical considerations, the integral conditions are completely natural, and they arise in mathematical modelling in cases where it is impossible to obtain information about the process occurring at the boundary of the region of its flow using direct measurements or when it is possible to measure only some averaged (integral) characteristics of the desired quantity.
In this article, we study the an inverse boundary value problem for a fourth order pseudo parabolic equation with periodic and integral condition to identify the time-dependent coefficients along with the solution function theoretically, i.e. existence and uniqueness.
Statement of the problem and its reduction to an equivalent problem. In the domain DT={(x,t):0≤x≤1,0≤t≤T}, we consider an inverse boundary value problem of recovering the timewise dependent coefficients p(t) in the pseudo-parabolic equation of the fourth-order
ut(x,t)−butxx(x,t)+a(t)uxxxx(x,t)=p(t)u(x,t)+f(x,t) | (1.1) |
with the initial condition
u(x,0)+δu(x,T)=φ(x)(0≤x≤1), | (1.2) |
boundary conditions
u(0,t)=u(1,t),ux(0,t)=ux(1,t),uxx(0,t)=uxx(1,t)(0≤t≤T), | (1.3) |
nonlocal integral condition
∫10u(x,t)dx=0(0≤t≤T) | (1.4) |
and with an additional condition
u(0,t)=∫t0γ(τ)u(1,τ)dτ+h(t)(0≤t≤T), | (1.5) |
where b>0, δ≥0-given numbers, a(t)>0,f(x,t),φ(x),γ(τ),h(t) -given functions, u(x,t) and p(t) - required functions.
Denote
ˉC4,1(DT)={u(x,t):u(x,t)∈C2,1(DT),utxx,uxxxx∈C(DT)}. |
Definition.By the classical solution of the inverse boundary value problem (1.1)-(1.5)we mean the pair {u(x,t),p(t)} functions u(x,t)∈ˉC4,1(DT), p(t)∈C[0,T] satisfying equation (1.1) in DT, condition (1.2) in [0, 1] and conditions (1.3)-(1.5) in [0, T].
Theorem 1. Let be b>0,δ≥0,φ(x)∈C[0,1],f(x,t)∈C(DT), ∫10f(x,t)dx=0, 0<a(t)∈C[0,T], h(t)∈C1[0,T], h(t)≠0(0≤t≤T), γ(t)∈C[0,T],δγ(t)=0 (0≤t≤T) and
∫10φ(x)dx=0,φ(0)=h(0)+δh(T). |
Then the problem of finding a solution to problem (1.1)-(1.5) is equivalent to the problem of determining the functions u(x,t)∈ˉC4,1(DT) and p(t)∈C[0,T], from (1.1)-(1.3) and
uxxx(0,t)=uxxx(1,t)(0≤t≤T), | (1.6) |
γ(t)u(1,t)+h′(t)−butxx(0,t)+a(t)uxxxx(0,t)= |
=p(t)(∫t0γ(τ)u(1,τ)dτ+h(t))+f(0,t)(0≤t≤T). | (1.7) |
Proof. Let be {u(x,t),p(t)} is a classical solution to problem (1.1)-(1.5). Integrating equation (1.1) with respect to x from 0 to 1, we get:
ddt∫10u(x,t)dx−b(utx(1,t)−utx(0,t))+a(t)(uxxx(1,t)−uxxx(0,t))= |
=p(t)∫10u(x,t)dx+∫10f(x,t)dx(0≤t≤T). | (1.8) |
Assuming that ∫10f(x,t)dx=0, taking into account (1.3) and (1.4), we arrive at the fulfillment of (1.6).
Further, considering h(t)∈C1[0,T] and differentiating with respect to t (1.5), we get:
ut(0,t)=γ(t)u(1,t)+h′(t)(0≤t≤T) | (1.9) |
Substituting x=0 into equation (1.1), we have:
ut(0,t)−butxx(0,t)+a(t)uxxxx(0,t)=p(t)u(0,t)+f(0,t)(0≤t≤T). | (1.10) |
Now, suppose that {u(x,t),p(t)} is a solution to problem (1.1)-(1.3), (1.6), (1.7). Then from (1.8), taking into account (1.3) and (1.6), we find:
ddt∫10u(x,t)dx−p(t)∫10u(x,t)dx=0(0≤t≤T). | (1.11) |
Due to (1.2) and ∫10φ(x)dx=0, it's obvious that
∫10u(x,0)dx+δ∫10u(x,T)dx=∫10φ(x)dx=0. | (1.12) |
Obviously, the general solution(1.11) has the form:
∫10u(x,t)dx=ce−∫t0p(τ)dτ(0≤t≤T). | (1.13) |
From here, taking into account (1.12), we obtain:
∫10u(x,0)dx+δ∫10u(x,T)dx=c(1+δe−∫T0p(τ)dτ)=0. | (1.14) |
By virtue of δ≥0, from (1.14) we get that c=0, and substituting into (1.13) we conclude, that ∫10u(x,t)dx=0(0≤t≤T). Therefore, condition (1.4) is also satisfied.
Further, from (1.7) and (1.10), we obtain:
ddt[u(0,t)−(∫t0γ(τ)u(1,τ)dτ+h(t))]= |
=p(t)[u(0,t)−(∫t0γ(τ)u(1,τ)dτ+h(t))](0≤t≤T). | (1.15) |
Let introduce the notation:
y(t)≡u(0,t)−(∫t0γ(τ)u(1,τ)dτ+h(t))(0≤t≤T) | (1.16) |
and rewrite the last relation in the form:
y′(t)+p(t)y(t)=0(0≤t≤T). | (1.17) |
From (1.16), taking into account (1.2), δγ(t)=0 (0≤t≤T) and φ(0)=h(0)+δh(T), it is easy to see that
y(0)+δy(T)=u(0,0)−h(0)+δ[u(0,T)−(∫T0γ(τ)u(1,τ)dτ+h(T))]=u(0,0)+ |
+δu(0,T)−(h(0)+δh(T))−δ∫T0γ(τ)u(1,τ)dτ=φ(0)−(h(0)+δh(T))=0. | (1.18) |
Obviously, the general solution (1.17) has the form:
y(t)=ce−∫t0p(τ)dτ(0≤t≤T). | (1.19) |
From here, taking into account (1.18), we obtain:
y(0)+δy(T)=c(1+δe−∫T0a0(τ)a1(τ)dτ)=0. | (1.20) |
By virtue of δ≥0, from (1.20) we get that c=0, and substituting into (1.19) we conclude that y(t)=0(0≤t≤T). Therefore, from (1.16) it is clear that the condition (1.5). The theorem has been proven.
It is known [5] that the system
1,cosλ1x,sinλ1x,...,cosλkx,sinλkx,... | (2.1) |
forms the basis of L2(0,1), where λk=2kπ(k=0,1,...).
Since system (2.1) forms a basis in L2(0,1), it is obvious that for each solution {u(x,t),a(t)} problem (1.1)–(1.3), (1.6), (1.7):
u(x,t)=∞∑k=0u1k(t)cosλkx+∞∑k=1u2k(t)sinλkx(λk=2πk), | (2.2) |
where
u10(t)=∫10u(x,t)dx,u1k(t)=2∫10u(x,t)cosλkxdx(k=1,2,...), |
u2k(t)=2∫10u(x,t)sinλkxdx(k=1,2,...). |
Applying the formal scheme of the Fourier method, to determine the desired coefficients u1k(t)(k=0,1,...) and u2k(t)(k=1,2,...) functions u(x,t) from (1.1) and (1.2) we get:
u″10(t)=F10(t;u,p)(0≤t≤T), | (2.3) |
(1+bλ2k)u′ik(t)+a(t)λ4kuik(t)=Fik(t;u,p)(i=1,2;0≤t≤T;k=1,2,...), | (2.4) |
u10(0)+δu10(T)=φ10, | (2.5) |
uik(0)+δuik(T)=φik(i=1,2;k=1,2,...), | (2.6) |
where
F1k(t;u,a,b)=p(t)u1k(t)+f1k(t)(k=0,1,...), |
f10(t)=∫10f(x,t)dx,f1k(t)=2∫10f(x,t)cosλkxdx(k=1,2,...), |
φ10=∫10φ(x)dx,φ1k=2∫10φ(x)cosλkxdx(k=1,2,...), |
F2k(t;u,a,b)=p(t)u2k(t)+f2k(t), |
f2k(t)=2∫10f(x,t)sinλkxdx(k=1,2,...),φ2k=2∫10φ(x)sinλkxdx(k=1,2,...). |
Solving problem (2.3)-(2.6), we find:
u10(t)=(1+δ)−1(φ10−δ∫T0F0(τ;u,p)dτ)+∫t0F10(τ;u,p)dτ(0≤t≤T), | (2.7) |
uik(t)=e−∫t0a(s)λ4k1+bλ2kds1+δe−∫T0a(s)λ4k1+bλ2kdssφik+11+bλ2k∫t0Fik(τ;u,p)e−∫tτa(s)λ4k1+bλ2kdsdτ− |
−δe−∫T0a(s)λ4k1+bλ2kds1+δe−∫T0a(s)λ4k1+bλ2kds11+bλ2k∫T0Fik(τ;u,p)e−∫tτa(s)λ4k1+bλ2kdsdτ(i=1,2;0≤t≤T;k=1,2,...). | (2.8) |
After substituting the expression u1k(t)(k=0,1,...), u2k(t)(k=1,2,...) in (2.2), to define a component u(x,t) solution of problem (1.1)-(1.3), (1.6), (1.7), we obtain:
u(x,t)=(1+δ)−1(φ0−δ∫T0F0(τ;u,p)dτ)+∫t0F0(τ;u,p)dτ+ |
+∞∑k=1{e−∫t0a(s)λ4k1+bλ2kds1+δe−∫T0a(s)λ4k1+bλ2kdssφ1kk+11+bλ2k∫t0F1k(τ;u,p)e−∫tτa(s)λ4k1+bλ2kdsdτ− |
−δe−∫T0a(s)λ4k1+bλ2kds1+δe−∫T0a(s)λ4k1+bλ2kds11+bλ2k∫T0F1k(τ;u,p)e−∫tτa(s)λ4k1+bλ2kdsdτ}cosλkx+ |
+∞∑k=1{e−∫t0a(s)λ4k1+bλ2kds1+δe−∫T0a(s)λ4k1+bλ2kdssφ2kk+11+bλ2k∫t0F2k(τ;u,p)e−∫tτa(s)λ4k1+bλ2kdsdτ− |
−δe−∫T0a(s)λ4k1+bλ2kds1+δe−∫T0a(s)λ4k1+bλ2kds11+bλ2k∫T0F2k(τ;u,p)e−∫tτa(s)λ4k1+bλ2kdsdτ}sinλkx. | (2.9) |
Now from (1.7), taking into account (2.2), we have:
p(t)=[h(t)]−1{h′(t)−f(0,t)+γ(t)u10(t)−p(t)∫t0γ(τ)u10(τ)dτ+ |
+∞∑k=1(bλ2ku′1k(t)+a(t)λ4ku1k(t)+γ(t)u1k(t)−p(t)∫t0γ(τ)u1k(τ)dτ). | (2.10) |
Further, from (2.4), taking into account (2.8), we obtain:
bλ2ku′1k(t)+a(t)λ4ku1k(t)+γ(t)u1k(t)=F1k(t;u,p)−u′1k(t)+γ(t)u1k(t)= |
=bλ2k1+bλ2kF1k(t;u,p)+(a(t)λ4k1+bλ2k+γ(t))u1k(t)= |
=bλ2k1+bλ2kFk(t;u,p)+(a(t)λ4k1+bλ2k+γ(t))[e−∫t0a(s)λ4k1+bλ2kds1+δe−∫T0a(s)λ4k1+bλ2kdssφ1k+ |
+11+bλ2k∫t0F1k(τ;u,p)e−∫tτa(s)λ4k1+bλ2kdsdτ− |
−δe−∫T0a(s)λ4k1+bλ2kds1+δe−∫T0a(s)λ4k1+bλ2kds11+bλ2k∫T0F1k(τ;u,p)e−∫tτa(s)λ4k1+bλ2kdsdτ](0≤t≤T;k=1,2,...). | (2.11) |
p(t)=[h(t)]−1{h′(t)−f(0,t)+ |
+γ(t))[(1+δ)−1(φ10−δ∫T0F0(τ;u,p)dτ)+∫t0F10(τ;u,p)dτ]− |
−p(t)∫t0γ(τ)u10(τ)dτ+∞∑k=1[bλ2k1+bλ2kF1k(t;u,p)+ |
+(a(t)λ4k1+bλ2k+γ(t))[e−∫t0a(s)λ4k1+bλ2kds1+δe−∫T0a(s)λ4k1+bλ2kdssφ1k+11+bλ2k∫t0F1k(τ;u,p)e−∫tτa(s)λ4k1+bλ2kdsdτ− |
+11+bλ2k∫t0F1k(τ;u,p)e−∫tτa(s)λ4k1+bλ2kdsdτ− |
−δe−∫T0a(s)λ4k1+bλ2kds1+δe−∫T0a(s)λ4k1+bλ2kds11+bλ2k∫T0F1k(τ;u,p)e−∫tτa(s)λ4k1+bλ2kdsdτ]+ |
+p(t)∫t0γ(τ)u1k(τ)dτ]}. | (2.12) |
Thus, the solution of problem (1.1)–(1.3), (1.6), (1.7)is reduced to the solution of system (2.9), (2.12) with respect to unknown functions u(x,t) and p(t).
To study the question of the uniqueness of the solution of problem (1.1)–(1.3), (1.6), (1.7) the following plays an important role.
Lemma 1. If {u(x,t),p(t)}-any solution of problem (1.1)–(1.3), (1.6), (1.7), then the functions
u10(t)=∫10u(x,t)dx,u1k(t)=2∫10u(x,t)cosλkxdx(k=1,2,...), |
u2k(t)=2∫10u(x,t)sinλkxdx(k=1,2,...) |
satisfy the system consisting of equations (27), (28) on [0,T].
It is obvious that if u10(t)=∫10u(x,t)dx, u1k(t)=2∫10u(x,t)cosλkxdx(k=1,2,...), u2k(t)=2∫10u(x,t)sinλkxdx(k=1,2,...) is a solution to system (2.7), (2.8), then the pair {u(x,t),p(t)} functions u(x,t)=∑∞k=0u1k(t)cosλkx+∑∞k=1u2k(t)sinλkx(λk=2πk) and p(t) is a solution to system (2.9), (2.12).
Consequence. Let system (29), (32) have a unique solution. Then problem (1.1)–(1.3), (1.6), (1.7) cannot have more than one solution, i.e. if problem (1.1)-(1.3), (1.6), (1.7) has a solution, then it is unique.
In order to study the problem (1.1)–(1.3), (1.6), (1.7) consider the following spaces.
Denote by Bα2,T [6] the set of all functions of the form
u(x,t)=∞∑k=0u1k(t)cosλkx+∞∑k=1u2k(t)sinλkx(λk=2πk), |
considered in DT, where each of the functions u1k(t)(k=0,1,...), u2k(t)(k=1,2,...) continuous on [0,T] and
J(u)=‖u10(t)‖C[0,T]+{∞∑k=1(λαk‖u1k(t)‖C[0,T])2}12+{∞∑k=1(λαk‖u2k(t)‖C[0,T])2}12<+∞, |
α≥0. We define the norm in this set as follows:
‖u(x,t)‖Bα2,T=J(u). |
Through EαT denote the space Bα2,T×C[0,T] vector - functions z(x,t)={u(x,t),p(t)} with norm
‖z(x,t)‖EαT=‖u(x,t)‖Bα2,T+‖p(t)‖C[0,T]. |
It is known that Bα2,T and EαT are Banach spaces.
Now consider in space E5T operator
Φ(u,p)={Φ1(u,p),Φ2(u,p)}, |
operator
Φ1(u,p))=˜u(x,t)≡∞∑k=0˜u1k(t)cosλkx+∞∑k=1˜u2k(t)sinλkx,Φ2(u,p)=˜p(t), |
˜u10(t),˜uik(t)(i=1,2;k=1,2,...),˜p(t) are equal to the right-hand sides of (2.7), (2.8) and (2.12), respectively.
It is easy to see that
1+bλ2k>bλ2k,1+δ≥1,.1+δe−∫T0a(s)λ4k1+bλ2kds≥1. |
Then, we have:
‖˜u0(t)‖C[0,T]≤|φ10|+(1+δ)√T(∫T0|f10(τ)|2dτ)12+(1+δ)T‖p(t)‖C[0,T]‖u10(t)‖C[0,T], | (2.13) |
(∞∑k=1(λ5k‖˜uik(t)‖C[0,T])2)12≤√3(∞∑k=1(λ5k|φik|)2)12+√3(1+δ)b√T(∫T0∞∑k=1(λ3k|fik(τ)|)2dτ)12+ |
+√3(1+δ)bT‖p(t)‖C[0,T](∞∑k=1(λ5k‖uik(t)‖C[0,T])2)12(i=1,2), | (2.14) |
‖˜p(t)‖C[0,T]≤‖[h(t)]−1‖C[0,T]{‖h′(t)−f(0,t)‖C[0,T]+ |
+‖γ(t)‖C[0,T][|φ0|+(1+δ)√T(∫T0|f0(τ)|2dτ)12+(1+δ)T‖p(t)‖C[0,T]‖u0(t)‖C[0,T]]+ |
+T‖γ(t)‖C[0,T]‖p(t)‖C[0,T]‖u10(t)‖C[0,T]+ |
+(∞∑k=1λ−2k)12[(∞∑k=1(λk‖f1k(t)‖)2C[0,T])12+‖p(t)‖C[0,T](∞∑k=1(λ3k‖u1k(t)‖C[0,T])2)12+ |
+(‖γ(t)‖C[0,T]+1b‖a(t)‖C[0,T])[(∞∑k=1(λ3k|φ1k|)2)12+√T(1+δ)b(∫T0∞∑k=1(λk|f1k(τ)|)2dτ)12+ |
+T(1+δ)b‖p(t)‖C[0,T](∞∑k=1(λ5k‖u1k(t)‖C[0,T])2)12]++T‖γ(t)‖C[0,T]‖p(t)‖C[0,T](∞∑k=1(λ5k‖u1k(t)‖C[0,T])2)12]}. | (2.15) |
Let us assume that the data of problem (1.1)–(1.3), (1.6), (1.7) satisfy the following conditions:
1.φ(x)∈W2(5)(0,1),φ(0)=φ(1),φ′(0)=φ′(1), |
φ″(0)=φ″(1),φ‴(0)=φ‴(1),φ(4)(0)=φ(4)(1); |
2.f(x,t),fx(x,t),fxx(x,t)∈C(DT),fxxx(x,t)∈L2(DT), |
f(0,t)=f(1,t),fx(0,t)=fx(1,t),fxx(0,t)=fxx(1,t)(0≤t≤T); |
3.b>0,δ≥0,γ(t),a(t)∈C[0,T],h(t)∈C1[0,T],h(t)≠0(0≤t≤T). |
Then from (2.10)–(2.12), we have:
‖˜u(x,t)‖B52,T≤A1(T)+B1(T)‖p(t)‖C[0,T]‖u(x,t)‖B52,T, | (2.16) |
‖˜p(t)‖C[0,T]≤A2(T)+B2(T)‖p(t)‖C[0,T]‖u(x,t)‖B52,T, | (2.17) |
where
A1(T)=‖φ(x)‖L2(0,1)+(1+δ)√T‖f(x,t)‖L2(DT)+2√3‖φ(5)(x)‖L2(0,1)+ |
+2√3b(1+δ)√T‖fxxx(x,t)‖L2(DT),B1(T)=(1+δ)(1+√3b)T, |
A2(T)=‖[h(t)]−1‖C[0,T]{‖h′(t)−f(0,t)‖C[0,T]+ |
+‖γ(t)‖C[0,T](‖φ(x)‖L2(0,1)+(1+δ)√T‖f(x,t)‖L2(DT))+ |
+(∞∑k=1λ−2k)12[‖‖fx(x,t)‖C[0,T]‖L2(0,1)+ |
+(‖γ(t)‖C[0,T]+1b‖a(t)‖C[0,T])(‖φ(3)(x)‖L2(0,1)+√T(1+δ)b‖fx(x,t)‖L2(DT))]}, |
B2(T)=‖[h(t)]−1‖C[0,T](∑∞k=1λ−2k)12[(‖γ(t)‖C[0,T]+1b‖a(t)‖C[0,T])T(2+δ)b+ |
+T‖γ(t)‖C[0,T]+1]. |
From inequalities (2.16), (2.17) we conclude:
‖u(x,t)‖B52,T+‖˜p(t)‖C[0,T]≤A(T)+B(T)‖p(t)‖C[0,T]‖u(x,t)‖B52,T, | (2.18) |
A(T)=A1(T)+A2(T),B(T)=B1(T)+B2(T). |
We can prove the following theorem.
Theorem 2. Let conditions 1-3 be satisfied and
(A(T)+2)2B(T)<1. | (2.19) |
Then problem (1.1)–(1.3), (1.6), (1.7) has in K=KR(‖z‖E5T≤R=A(T)+2) in the space E5T only one solution.
Proof. In space E5T consider the equation
z=Φz, | (2.20) |
where z={u,p}, components P Φ1(u,p),Φ2(u,p) of operators Φ(u,p) are defined by the right-hand sides of equations (2.9) and (2.12).
Consider the operator Φ(u,p) in a ball K=KR from E5T. Similarly to (2.18) we obtain that for any z={u,p}, z1={u1,p1}, z2={u2,p2}∈KR :
‖Φz‖E5T≤A(T)+B(T)‖p(t)‖C[0,T]‖u(x,t)‖B52,T, | (2.21) |
‖Φz1−Φz2‖E5T≤B(T)R(‖p1(t)−p2(t)‖C[0,T]+‖u1(x,t)−u2(x,t)‖B52,T). | (2.22) |
Then from estimates (2.21), (2.22), taking into account (2.19), it follows that the operator Φ acts in a ball K=KR and is contractive. Therefore, in the ball K=KR operator Φ has a single fixed point {u,p}, which is the only one in the ball K=KR solution of equation (2.20), i.e. is the only one solution in the ball K=KR of system (2.9), (2.12) in the ball.
Functions u(x,t), as an element of space B52,T is continuous and has continuous derivatives ux(x,t),uxx(x,t), uxxx(x,t),uxxxx(x,t) in DT.
From (2.4), it is easy to see that
(∞∑k=1(λk‖u′ik(t)‖C[0,T])2)12≤√2b‖a(t)‖C[0,T](∞∑k=1(λ5k‖uik(t)‖C[0,T])2)12+ |
+√2b‖‖fx(x,t)+p(t)ux(x,t)‖C[0,T]‖L2(0,1)(i=1,2). |
Hence it follows that ut(x,t) and utxx continuous in DT.
It is easy to check that equation (1.1) and conditions (1.2), (1.3), (1.6), (1.7) are satisfied in the usual sense. Consequently, {u(x,t),p(t)} is a solution to problem (1.1)–(1.3), (1.6), (1.7). By the corollary of Lemma 1, it is unique in the ball K=KR. The theorem has been proven.
With the help of Theorem 1, the unique solvability of the original problem (1.1)–(1.5) immediately follows from the last theorem.
Theorem 3. Let all the conditions of Theorem 1 be satisfied, ∫10f(x,t)dx=0(0≤t≤T), δγ(t)=0 (0≤t≤T) and the matching condition is met:
∫10φ(x)dx=0,φ(0)=h(0)+δh(T). |
Then problem (1.1)–(1.5) has in the ball K=KR(‖z‖E5T≤R=A(T)+2) from E5T the only classical solution.
The article considered an inverse boundary value problem with a periodic and integral condition, when the unknown coefficient depends on time for a linear pseudoparabolic equation of the fourth order. An existence and uniqueness theorem for the classical solution of the problem is proved.
The authors have declared no conflict of interest.
[1] | T. L. Saaty, The analytic hierarchy process, New York: McGraw-Hill, 1980. https://doi.org/10.21236/ADA214804 |
[2] |
T. Tanino, Fuzzy preference orderings in group decision making, Fuzzy Set. Syst., 12 (1984), 117–131. https://doi.org/10.1016/0165-0114(84)90032-0 doi: 10.1016/0165-0114(84)90032-0
![]() |
[3] |
C. C. Li, Y. C. Dong, Y. J. Xu, F. Chiclana, E. Herrera-Viedma, F. Herrera, An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and fusion: Taxonomy and future directions, Inform. Fusion, 52 (2019), 143–156. https://doi.org/10.1016/j.inffus.2018.12.004 doi: 10.1016/j.inffus.2018.12.004
![]() |
[4] |
Y. J. Xu, Q. Q. Wang, F. Chiclana, E. Herrera-Viedma, A local adjustment method to improve multiplicative consistency of fuzzy reciprocal preference relations, IEEE Trans. Syst. Man Cy.-S., 53 (2023), 5702–5714. https://doi.org/10.1109/TSMC.2023.3275167 doi: 10.1109/TSMC.2023.3275167
![]() |
[5] |
M. Q. Li, Z. Y. Wang, Y. J. Xu, W. J. Dai, Two-stage group decision making methodology with hesitant fuzzy preference relations under social network: Multiplicative consistency determination and personalized feedback, Inform. Sciences, 681 (2024), 121155. https://doi.org/10.1016/j.ins.2024.121155 doi: 10.1016/j.ins.2024.121155
![]() |
[6] |
X. Liu, Y. Y. Zhang, Y. J. Xu, M. Q. Li, E. Herrera-Viedma, A consensus model for group decision-making with personalized individual self-confidence and trust semantics: A perspective on dynamic social network interactions, Inform. Sciences, 627 (2023), 147–168. https://doi.org/10.1016/j.ins.2023.01.087 doi: 10.1016/j.ins.2023.01.087
![]() |
[7] |
F. Herrera, A sequential selection process in group decision making with a linguistic assessment approach, Inform. Sciences, 85 (1995), 223–239. https://doi.org/10.1016/0020-0255(95)00025-K doi: 10.1016/0020-0255(95)00025-K
![]() |
[8] |
V. Torra, Hesitant fuzzy sets, Int. J. Intell. Syst., 25 (2010), 529–539. https://doi.org/10.1002/int.20418 doi: 10.1002/int.20418
![]() |
[9] |
M. W. Jang, J. H. Park, M. J. Son, Probabilistic picture hesitant fuzzy sets and their application to multi-criteria decision-making, AIMS Math., 8 (2023), 8522–8559. https://doi.org/10.3934/math.2023429 doi: 10.3934/math.2023429
![]() |
[10] |
A. Nazra, Jenison, Y. Asdi, Zulvera, Generalized hesitant intuitionistic fuzzy N-soft sets-first result, AIMS Math., 7 (2022), 12650–12670. https://doi.org/10.3934/math.2022700 doi: 10.3934/math.2022700
![]() |
[11] |
W. Y. Zeng, R. Ma, D. Q. Li, Q. Yin, Z. S. Xu, A. M. Khalil, Novel operations of weighted hesitant fuzzy sets and their group decision making application, AIMS Math., 7 (2022), 14117–14138. https://doi.org/10.3934/math.2022778 doi: 10.3934/math.2022778
![]() |
[12] |
A. Dey, T. Senapati, M. Pal, G. Y. Chen, A novel approach to hesitant multi-fuzzy soft set based decision-making, AIMS Math., 5 (2020), 1985–2008. https://doi.org/10.3934/math.2020132 doi: 10.3934/math.2020132
![]() |
[13] |
R. M. Rodríguez, L. Martı́nez, F. Herrera, Hesitant fuzzy linguistic term sets for decision making, IEEE T. Fuzzy Syst., 20 (2012), 109–119. https://doi.org/10.1109/TFUZZ.2011.2170076 doi: 10.1109/TFUZZ.2011.2170076
![]() |
[14] |
M. M. Xia, Z. S. Xu, Managing hesitant information in GDM problems under fuzzy and multiplicative preference relations, Int. J. Uncertain. Fuzz., 21 (2013), 865–897. https://doi.org/10.1142/S0218488513500402 doi: 10.1142/S0218488513500402
![]() |
[15] |
Y. J. Xu, W. J. Dai, J. Huang, M. Q. Li, E. Herrera-Viedma, Some models to manage additive consistency and derive priority weights from hesitant fuzzy preference relations, Inform. Sciences, 586 (2022), 450–467. https://doi.org/10.1016/j.ins.2021.12.002 doi: 10.1016/j.ins.2021.12.002
![]() |
[16] |
Y. J. Xu, M. Q. Li, F. Chiclana, E. Herrera-Viedma, Multiplicative consistency ascertaining, inconsistency repairing, and weights derivation of hesitant multiplicative preference relations, IEEE T. Syst. Man Cy-S., 52 (2022), 6806–6821. https://doi.org/10.1109/TSMC.2021.3099862 doi: 10.1109/TSMC.2021.3099862
![]() |
[17] |
B. Zhu, Z. S. Xu, Consistency measures for hesitant fuzzy linguistic preference relation, IEEE T. Fuzzy Syst., 22 (2014), 35–45. https://doi.org/10.1109/TFUZZ.2013.2245136 doi: 10.1109/TFUZZ.2013.2245136
![]() |
[18] |
Z. M. Zhang, C. Wu, On the use of multiplicative consistency in hesitant fuzzy linguistic preference relations, Knowl.-Based Syst., 72 (2014), 13–27. http://dx.doi.org/10.1016/j.knosys.2014.08.026 doi: 10.1016/j.knosys.2014.08.026
![]() |
[19] |
Z. B. Wu, J. P. Xu, Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations, Omega, 65 (2016), 28–40. http://dx.doi.org/10.1016/j.omega.2015.12.005 doi: 10.1016/j.omega.2015.12.005
![]() |
[20] |
X. Chen, L. J. Peng, Z. B. Wu, W. Pedrycz, Controlling the worst consistency index for hesitant fuzzy linguistic preference relations in consensus optimization models, Comput. Ind. Eng., 143 (2020), 106423. https://doi.org/10.1016/j.cie.2020.106423 doi: 10.1016/j.cie.2020.106423
![]() |
[21] |
C. L. Zheng, Y. Y. Zhou, L. G. Zhou, H. Y. Chen, Clustering and compatibility-based approach for large-scale group decision making with hesitant fuzzy linguistic preference relations: An application in e-waste recycling, Expert Syst. with Appl., 197 (2022), 116615. https://doi.org/10.1016/j.eswa.2022.116615 doi: 10.1016/j.eswa.2022.116615
![]() |
[22] |
P. Wu, L. G. Zhou, H. Y. Chen, Z. F. Tao, Additive consistency of hesitant fuzzy linguistic preference relation with a new expansion principle for hesitant fuzzy linguistic term sets, IEEE T. Fuzzy Syst., 27 (2019), 716–730. https://doi.org/10.1109/TFUZZ.2018.2868492 doi: 10.1109/TFUZZ.2018.2868492
![]() |
[23] |
Y. J. Xu, F. J. Cabrerizo, E. Herrera-Viedma, A consensus model for hesitant fuzzy preference relations and itsapplication in water allocation management, Appl. Soft Comput., 58 (2017), 265–284. https://doi.org/10.1016/j.asoc.2017.04.068 doi: 10.1016/j.asoc.2017.04.068
![]() |
[24] |
C. C. Li, R. M. Rodríguez, F. Herrera, L. Martínez, Y. C. Dong, Consistency of hesitant fuzzy linguistic preference relations: An interval consistency index, Inform. Sciences, 432 (2018), 347–361. https://doi.org/10.1016/j.ins.2017.12.018 doi: 10.1016/j.ins.2017.12.018
![]() |
[25] |
C. C. Li, R. M. Rodríguez, L. Martínez, Y. C. Dong, F. Herrera, Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions, Knowl.-Based Syst., 145 (2018), 156–165. https://doi.org/10.1016/j.knosys.2018.01.011 doi: 10.1016/j.knosys.2018.01.011
![]() |
[26] |
Y. J. Xu, X. W. Wen, H. Sun, H. M. Wang, Consistency and consensus models with local adjustment strategy for hesitant fuzzy linguistic preference relations, Int. J. Fuzzy Syst., 20 (2018), 2216–2233. https://doi.org/10.1007/s40815-017-0438-3 doi: 10.1007/s40815-017-0438-3
![]() |
[27] |
H. B. Liu, L. Jiang, Optimizing consistency and consensus improvement process for hesitant fuzzy linguistic preference relations and the application in group decision making, Inform. Fusion, 56 (2020), 114–127. https://doi.org/10.1016/j.inffus.2019.10.002 doi: 10.1016/j.inffus.2019.10.002
![]() |
[28] |
M. Fedrizz, S. Giove, Incomplete pairwise comparison and consistency optimization, Eur. J. Oper. Res., 183 (2007), 303–313. https://doi.org/10.1016/j.ejor.2006.09.065 doi: 10.1016/j.ejor.2006.09.065
![]() |
[29] |
Z. S. Xu, Incomplete linguistic preference relations and their fusion, Inform. Fusion, 7 (2006), 331–337. https://doi.org/10.1016/j.inffus.2005.01.003 doi: 10.1016/j.inffus.2005.01.003
![]() |
[30] |
Y. J. Xu, C. Y. Li, X. W. Wen, Missing values estimation and consensus building for incomplete hesitant fuzzy preference relations with multiplicative consistency, Int. J. Comput. Int. Syst., 11 (2018), 101–119. https://doi.org/10.2991/ijcis.11.1.9 doi: 10.2991/ijcis.11.1.9
![]() |
[31] |
Y. L. Lu, Y. J. Xu, E. Herrera-Viedma, Consensus progress for large-scale group decision making in social networks with incomplete probabilistic hesitant fuzzy information, Appl. Soft Comput., 126 (2022), 109249. https://doi.org/10.1016/j.asoc.2022.109249 doi: 10.1016/j.asoc.2022.109249
![]() |
[32] |
Y. L. Lu, Y. J. Xu, J. Huang, J. Wei, Social network clustering and consensus-based distrust behaviors management for large-scale group decision-making with incomplete hesitant fuzzy preference relations, Appl. Soft Comput., 117 (2022), 108373. https://doi.org/10.1016/j.asoc.2021.108373 doi: 10.1016/j.asoc.2021.108373
![]() |
[33] |
J. Huang, Y. J. Xu, X. W. Wen, X. T. Zhu, E. Herrera-Viedma, Deriving priorities from the fuzzy best-worst method matrix and its applications: A perspective of incomplete reciprocal preference relation, Inform. Sciences, 634 (2023), 761–778. https://doi.org/10.1016/j.ins.2023.03.125 doi: 10.1016/j.ins.2023.03.125
![]() |
[34] |
P. Wu, H. Y. Li, J. M. Merigó, L. G. Zhou, Integer programming modeling on group decision making with incomplete hesitant fuzzy linguistic preference relations, IEEE Access, 7 (2019), 136867–136881. https://doi.org/10.1109/ACCESS.2019.2942412 doi: 10.1109/ACCESS.2019.2942412
![]() |
[35] |
H. B. Liu, Y. Ma, L. Jiang, Managing incomplete preferences and consistency improvement in hesitant fuzzy linguistic preference relations with applications in group decision making, Inform. Fusion, 51 (2019), 19–29. https://doi.org/10.1016/j.inffus.2018.10.011 doi: 10.1016/j.inffus.2018.10.011
![]() |
[36] |
Z. L. Li, Z. Zhang, W. Y. Yu, Consensus reaching with consistency control in group decision making with incomplete hesitant fuzzy linguistic preference relations, Comput. Ind. Eng., 170 (2022), 108311. https://doi.org/10.1016/j.cie.2022.108311 doi: 10.1016/j.cie.2022.108311
![]() |
[37] |
P. J. Ren, Z. N. Hao, X. X. Wang, X. J. Zeng, Z. S. Xu, Decision-making models based on incomplete hesitant fuzzy linguistic preference relation with application to site selection of hydropower stations, IEEE T. Eng. Manage., 69 (2022), 904–915. https://doi.org/10.1109/TEM.2019.2962180 doi: 10.1109/TEM.2019.2962180
![]() |
[38] |
Y. M. Song, G. X. Li, A mathematical programming approach to manage group decision making with incomplete hesitant fuzzy linguistic preference relations, Comput. Ind. Eng., 135 (2019), 467–475. https://doi.org/10.1016/j.cie.2019.06.036 doi: 10.1016/j.cie.2019.06.036
![]() |
[39] |
F. Herrera, E. Herrera-Viedma, J. L. Verdegay, Direct approach processes in group decision making using linguistic OWA operators, Fuzzy Set. Syst., 79 (1994), 175–190. https://doi.org/10.1016/0165-0114(95)00162-X doi: 10.1016/0165-0114(95)00162-X
![]() |
[40] |
Z. S. Xu, A method based on linguistic aggregation operators for group decision making with linguistic preference relations, Inform. Sciences, 166 (2004), 19–30. https://doi.org/10.1016/j.ins.2003.10.006 doi: 10.1016/j.ins.2003.10.006
![]() |
[41] |
Y. C. Dong, Xu, Y. F., H. Y. Li, On consistency measures of linguistic preference relations, Eur. J. Oper. Res., 189 (2008), 430–444. https://doi.org/10.1016/j.ejor.2007.06.013 doi: 10.1016/j.ejor.2007.06.013
![]() |
[42] |
H. Wang, Extended hesitant fuzzy linguistic term sets and their aggregation in group decision making, Int. J. Comput. Int. Syst., 8 (2015), 14–33. https://doi.org/10.1016/j.ejor.2007.06.013 doi: 10.1016/j.ejor.2007.06.013
![]() |
[43] |
H. C. Liao, Z. S. Xu, X.-J. Zeng, J. M. Merigó, Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets, Knowl.-Based Syst., 76 (2015), 127–138. http://dx.doi.org/10.1016/j.knosys.2014.12.009 doi: 10.1016/j.knosys.2014.12.009
![]() |
[44] |
E. Herrera-Viedma, F. Herrera, F. Chiclana, M. Luque, Some issues on consistency of fuzzy preference relations, Eur. J. Oper. Res., 154 (2004), 98–109. https://doi.org/10.1016/S0377-2217(02)00725-7 doi: 10.1016/S0377-2217(02)00725-7
![]() |
[45] |
Y. J. Xu, K. W. Li, H. M. Wang, Incomplete interval fuzzy preference relations and their applications, Comput. Ind. Eng., 67 (2014), 93–103. https://doi.org/10.1016/j.cie.2013.10.010 doi: 10.1016/j.cie.2013.10.010
![]() |
[46] |
M. Tang, H. C. Liao, Z. M. Li, Z. S. Xu, Nature disaster risk evaluation with a group decision making method based on incomplete hesitant fuzzy linguistic preference relations, Int. J. Env. Res. Pub. He, 15 (2018), 751. https://doi.org/10.3390/ijerph15040751 doi: 10.3390/ijerph15040751
![]() |
[47] |
Y. J. Xu, F. Ma, F. Tao, H. M. Wang, Some methods to deal with unacceptable incomplete 2-tuple fuzzy linguistic preference relations in group decision making, Knowl.-Based Syst., 56 (2014), 179–190. http://dx.doi.org/10.1016/j.knosys.2013.11.008 doi: 10.1016/j.knosys.2013.11.008
![]() |
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