1. Introduction
Consider the random operator
. A natural generalization of parametric random equations of the form
, in which
is a element of a probability measure space, is the multi-valued form [
1],
In regards to solutions, there are many approaches available in the literature, for example the principal eigenvalue-eigenvector method, the monotone minorant method [
2,
3] and topological degree. The idea in this paper is to use the topological degree for random multi-valued mappings and the method of evaluating solutions. The main idea is presenting an uncertain case of a control problem. To achieve this aim, we use a special space, i.e., symmetric F-
n-NLS, that has a dynamic situation and a parameter
, which can be time, which enable us to consider different cases. We note this kind of space induced by a dynamic norm which is inspired by random norms, probabilistic distances and fuzzy norms was studied; see [
4] for details and applications. Our results can be applied in uncertainty problems, risk measures and super-hedging in finance [
5].
For the random multi-valued operator
, the following sets
or
are solutions of (
1). In this paper, we consider a control problem with multi-point boundary conditions and a second order derivative operator as
where
,
and
. In
Section 2, we introduce our special space, i.e., symmetric F-
n-NLS and present some basic results which we need in the main section. In
Section 3, we prove some properties of random multi-valued operator. In
Section 4, we present an application of our results for a fuzzy control problem.
2. Preliminaries
Here, we let , , and .
A mapping
, whose
-level set is denoted by
is said to be a fuzzy real number if it satisfies the following:
- (i)
is normal, i.e., there exists such that ;
- (ii)
is upper semicontinuous;
- (iii)
is fuzzy convex, i.e., , for each such that and ;
- (iv)
For each , , where and is compact.
Let the set
contain all upper semicontinuous normal convex fuzzy real numbers.
contains all non-negative fuzzy real numbers of
. For each
, we can define
so
and
can be embedded in
.
A partial order ⪯ in
is defined as follows:
iff for each
,
and
where
and
. The strict inequality in
is defined by
iff for each
,
and
(see [
6,
7,
8]).
The arithmetic operations ⊕, ⊖, ⊙ and ⊘ on
are defined by
Definition 1. Let ℧ be a real linear space over with dim . Suppose is a mapping and are symmetric, nondecreasing mapping satisfying Writefor , and suppose that for every linearly independent vectors , there exists independent of such that for each , one has The quadruple is said to be a symmetric fuzzy n-normed linear space (F-n-NLS) in the sense of Felbin [8] and is a fuzzy n-norm if - (N1)
iff are linearly dependent;
- (N2)
is invariant under any permutation of ;
- (N3)
for any ;
- (N4)
;
- (i)
whenever , and , - (ii)
whenever , and ,
Now, we consider a symmetric F-
n-NLS in the sense of Narayanan-Vijayabalaji [
9] and next we show a relationship between them.
Definition 2 ([
9])
. Assume that ℧ is a linear space and ∗ is a continuous t-norm. Let the fuzzy subset η of with dim satisfy- (FN1)
For all with , ;
- (FN2)
For all with , for iff are linearly dependent;
- (FN3)
is invariant under any permutation of ;
- (FN4)
For all with , - (FN5)
For all with , - (FN6)
is left continuous;
- (FN7)
.
Thus, the triple is a symmetric F-n-NLS (see [10,11,12]). A complete symmetric F-n-NLS is called symmetric F-n-BS.
Theorem 1 ([
9,
13,
14,
15])
. Let be a symmetric F-n-NLS in which and- (FN8)
for all implies are linearly dependent.
Then is an ascending family of fuzzy n-norms on ℧.
These fuzzy n-norms will be called the ϵ-n-norms on ℧ corresponding to the fuzzy n-norm on ℧.
We note that some applications can be found on [
16,
17].
Remark 1 ([
18])
. Let be a Euclidean fuzzy norm (Euclidean fuzzy normed spaces were introduced by the authors in [18]). Then are linearly independent iff , for any . By the above remark, we have that,
are linearly independent iff
Consider the probability measure space
and let
and
be Borel measurable spaces, where
U and
S are symmetric F-
n-BS. If
for every
in
U and
, we say
is a random operator. Let
be the family of all subsets of
S. The mapping
is said to be random multi-valued operator. A random operator
is said to be
linear if
almost everywhere for each
in
U and
are scalers, and
bounded if there exists a nonnegative real-valued random variable
such that
almost everywhere for each
in
U,
and
.
Let
be a symmetric F-
n-BS over
with dim
and ordering by the cone
, i.e.,
is a closed convex subset of
such that
for
,
, and
iff
for
with
and
. For nonempty subsets
of
we write
(or,
) iff for every
, we can find a
which
(or,
) for
. We say
is a normal cone if we can find a constant
where
for
implies
. We note in this paper, we consider
as a normal cone with
. Furthermore,
Consider the open convex subset
of
, and let
,
and
, where
is boundary of
in
. The mapping
is said to be compact if
is relatively compact for any bounded subset
of
, where
, for any
. We say a random multi-valued operator
has the upper semi-continuity property (in short, u.s.c.rmvo) if
where
and
. Further, if
for all
and
, the random fixed point index of
in
with respect to
is defined which is an integer denoted by
.
Lemma 1. [
2]
Let be a compact u.s.c.rmvo. Then- 1.
if there exists such that for all , and .
- 2.
if for all and .
The following results are needed later to obtain a generalization of [
19].
Lemma 2. [
20]
Assume that is a u.s.c.rmvo, with and . Thus, . Lemma 3. [
19]
Let be a compact u.s.c.rmvo with for all and . Then, . 3. Random Multi-Valued Operator
Lemma 4. Let be a compact u.s.c.rmvo and be open with . Additionally,
- 1.
, for any , for some implies ,
- 2.
if γ is sufficiently large and .
Then .
Proof. From the second condition in Lemma 4 we can find
such that
for all
and
. Define
We first observe that
. Furthermore,
for any
. Since
is compact, without loss of generality we may assume that
when
and
. From (
5) by Lemma 2 it follows that
This contradicts the first condition in Lemma 4. Thus, there exist
such that
for all
and
, where
Using Lemma 1 implies that . Thus, , and we deduce , for each .
Next, for every
and
, there exists
with
. Consider the random multi-valued operator
defined by
Assume the contrary, that
Then, the random fixed point index of
is well defined, for each
. If
then, by Lemma 3 we obtain
for each
, a contradiction. Then, we can find a
satisfying
for each
. Similarly, there is a
with
, which shows (
6) is not true, and completes the proof. □
Let be asymmetric F-n-BS over with dim ordered by the normal cone . Suppose that , the embedding is continuous, and is a compact u.s.c.rmvo. Assume is a compact random linear operator satisfying such that , for each .
Theorem 2. Let
- 1.
, for any , for some implies ;
- 2.
we can find positive numbers and a random linear operator with , for any , for some such that
- (a)
andfor all and , - (b)
for all , , and
- (c)
we can find an increasing map ( on the second part) such thatsuch that withimplies
Thenis an unbounded continuous branch emanating from 0, for each . Proof. Suppose
is open and bounded where
. We use Lemma 4 with
to show
, for any
. Clearly, condition 1 of Lemma 4 holds. Assume that
satisfies (
11), so
, hence,
, for any
, for some
. By 2(a) and 2(b) we have
and
for each
. For sufficiently large
, (
13) and (
14) we conclude that
which combining with (
12) gives
for each
. If
,
for some
. From (
15), (
11) it follows that
for all
and
, where
. Applying Lemma 1 we obtain
, here
, and therefore
, so condition 2 of Lemma 4 holds. The proof is complete. □
4. Applications
In this section, we study an uncertain case of a control problem. For it, we consider the compact u.s.c. rmvo
and the continuous map
. We consider the following control problem which contains a parameter:
where
,
and
.
Denote
for every
, and let
Let
, resp.,
, be the symmetric F-
n-BS of all continuous, resp., continuously differentiable, functions on
. Denote
and
Let
be a random linear operator (
) defined by
for each
and
. We solve
where the random multi-valued operator
is defined by
for each
and
, since it is equivalent (
17).
Theorem 3. Let . Suppose we can find , and such that
- 1.
,
- 2.
,
- 3.
.
Thus, the positive fuzzy solution set for (18) is unbounded continuous in , originating from 0. Proof. Use Theorem 2 and cones
and
Then,
and
, resp., are symmetric F-
n-BSs with the norms
and
Suppose
,
and
satisfies
, so we can find
such that
for each
and
. Then
. By [
21], we can conclude that the compact random linear operator
have an eigen-value
and a positive eigen-map
. Define the random linear operator
on
, by
. From condition 2. we have
for each
; here
. When
in which
and
, then we can find a number
such that
on
. For
,
is a concave function with
and
, and we have
, for each
. From Fubini’s Theorem it follows that
for each
. Consequently, there is constant
satisfying
for each
. Now, assume
with
This implies
for each
. Now,
and
n are constant, not depending on
and
. Using Theorem 2 implies that
Therefore we can choose
such that
From (
22), the well-known inequality
and (
21) we obtain
Furthermore, for
, we have
Combining the inequalities, (
22), (
23), (
24) and (
25) we get
From (
23) we can choose
n such that
Since we have condition (2c) of Theorem 2 satisfied with the function . □