96 Z. Zhang et al. / Neural Networks 25 (2012) 94–105
methods and techniques in order to obtain the global asymptotic
result of the equilibrium point for system (1.5).
The paper is organized as follows. In Section 2, a novel sufficient
condition is derived for the existence and uniqueness of the
equilibrium point for system (1.5). In Section 3, a novel sufficient
condition is derived for the global asymptotic stability of system
(1.5). In Section 4, an example and its simulation are given to show
the effectiveness of the obtained results. Finally, in Section 5, some
conclusions are provided.
For the sake of convenience, we introduce some notations and
definitions as follows.
For any matrix A, A
T
stands for the transpose of A and A
−1
denotes the inverse of A. If A is a symmetric matrix, A >
0 (A ≥ 0) means that A is positive definite (positive semidefinite).
Similarly, A < 0 (A ≤ 0) means that A is negative definite
(negative semidefinite). λ
M
(A), λ
m
(A) denote the maximum and
the minimum eigenvalue of a square matrix A. |A| = (|a
ij
|), |x| =
(|x
1
|, . . . , |x
m
|)
T
, ‖A‖ =
λ
M
(A
T
A). Let R
m
be an m-dimensional
Euclidean space, which is endowed with a norm ‖.‖ and inner
product (., .). Given column vector x = (x
1
, x
2
, . . . , x
m
)
T
∈ R
m
,
the norm is the Euclidean vector norm, i.e., ‖x‖ =
∑
m
i=1
x
2
i
1
2
.
Definition 1.1. A point (x
∗
, y
∗
)
T
∈ R
m
× R
m
is said to be an
equilibrium point of system (1.5) if
a
i
(x
∗
i
)
b
i
(x
∗
i
) −
m
−
j=1
s
ij
f
j
(x
∗
j
, y
∗
j
) + I
i
= 0, i = 1, 2, . . . , m,
c
j
(y
∗
j
)
d
j
(y
∗
j
) −
n
−
i=1
t
ji
g
i
(x
∗
i
, y
∗
i
) + J
j
= 0, j = 1, 2, . . . , m,
where x
∗
= (x
∗
1
, x
∗
2
, . . . , x
∗
m
)
T
, y
∗
= (y
∗
1
, y
∗
2
, . . . , y
∗
m
)
T
.
Lemma 1 (Forti & Tesi, 1995). Let H : R
2m
→ R
2m
be continuous.
Assume that H satisfies the following conditions
1. H(u) is injective on R
2m
,
2. ‖H(u)‖ → ∞ as ‖u‖ → ∞.
Then H is a homeomorphism.
Lemma 2. If a > 0, b > 0, β > 2, then
a
2
1
+ a
2
2
2
1
2
≤
a
β
1
+ a
β
2
2
1
β
.
Proof. Set αβ = 2 (0 < α < 1), then
2
−
i=1
a
2
i
2
=
2
−
i=1
1
2
α
a
αβ
i
1
2
1−α
≤
2
−
i=1
a
β
i
2
α
2
−
i=1
1
2
1−α
=
2
−
i=1
a
β
i
2
α
.
Hence
2
−
i=1
a
2
i
2
1
2
≤
2
−
i=1
a
β
i
2
α
2
=
2
−
i=1
a
β
i
2
1
β
.
Lemma 3. If a > 1, x > 0, then
x
a
≥ ax + 1 − a.
Proof. Set f (x) = x
a
− ax, x > 0. Then f
′
(x) = a(x
a−1
− 1). Since
a > 1, when 0 < x < 1, f
′
(x) < 0 and when x > 1, f
′
(x) > 0.
Hence, f (1) = 1 − a = min f (x). Namely x > 0, f (x) > 1 − a.
Lemma 4. If a > 0, b > 0, then
0 < p < 1, (a + b)
p
< a
p
+ b
p
.
Proof. This is a well-known inequality and its proof is omitted.
Lemma 5. If a > 0, b > 0,
1
p
+
1
q
= 1, then
ab ≤
a
p
p
+
b
q
q
.
Proof. This is a well-known inequality and its proof is omitted.
Lemma 6. If k > 1,
1
k
+
1
k
′
= 1, a
i
, b
i
∈ R, then
m
−
i=1
|a
i
| |b
i
| ≤
m
−
i=1
|a
i
|
k
1
k
m
−
i=1
|b
i
|
k
′
1
k
′
.
Proof. This is a well-known inequality and its proof is omitted.
Lemma 7. Assume that m and r are two positive integers, x
i
, y
i
∈ R,
then for i = 1, 2, . . . , m,
m
−
i=1
(|x
i
|
2r−1
+ |y
i
|
2r−1
) < A
∗
m
−
i=1
|x
i
|
2r
+ |y
i
|
2r
2r−1
2r
,
where A
∗
= m
1
2r
∑
2r
k=0
|C
k
2r
|
1
2r−1
max
k
2r
,
2r−k
2r
2r−1
2r
.
Proof. By using Lemmas 4 and 5, we obtain
m
−
i=1
(|x
i
|
2r−1
+ |y
i
|
2r−1
)
=
m
−
i=1
|x
i
|
2r−1
+
m
−
i=1
|y
i
|
2r−1
2r
2r−1
2r−1
2r
=
2r
−
k=0
C
k
2r
m
−
i=1
|x
i
|
2r−1
k
m
−
i=1
|y
i
|
2r−1
(2r−k)
1
2r−1
2r−1
2r
≤
2r
−
k=0
|C
k
2r
|
1
2r−1
m
−
i=1
|x
i
|
2r−1
k
2r−1
m
−
i=1
|y
i
|
2r−1
2r−k
2r−1
2r−1
2r
≤
2r
−
k=0
|C
k
2r
|
1
2r−1
k
2r
m
−
i=1
|x
i
|
2r−1
2r
2r−1
+
2r − k
2r
m
−
i=1
|y
i
|
2r−1
2r
2r−1
2r−1
2r
. (1.6)
Using Lemma 6, we have
m
−
i=1
|x
i
|
2r−1
≤
m
−
i=1
1
1
2r
m
−
i=1
(|x
i
|
2r−1
)
2r
2r−1
2r−1
2r
= m
1
2r
m
−
i=1
|x
i
|
2r
2r−1
2r
(1.7)
and