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4 One Example
U
=
0
L
=
diag
(
),
C
=
diag
(
.
.
.
),
For system (1), taking
2
Q
=
diag
(
0
.
.
.
),
0
0
−
0
−
0
0
0
⎛
⎞
⎛
⎞
⎜
⎟
⎜
⎟
A
=
0
−
0
0
,
B
=
0
0
−
0
,
⎜
⎟
⎜
⎟
⎜
⎟
⎜
⎟
−
0
0
0
0
−
0
−
0
⎝
⎠
⎝
⎠
0
0
0
⎛
⎞
⎜
⎟
P
=
0
0
0
.
⎜
⎟
⎜
⎟
0
0
0
⎝
⎠
U
=
diag
(
13
.
4497
,
13
.
6833
,
13
.
3997
),
One computes
1
4
2058
−
0
3947
0
2864
⎛
⎞
⎜
⎟
M
=
−
0
3947
4
7899
−
0
1674
.
⎜
⎟
⎜
⎟
0
2864
−
0
1674
4
0809
⎝
⎠
HH
and the solution of such system will approach
KerW
asymptotically with probability 1.
≥
≥
0
Therefore,
1
2
Acknowledgments.
This work was supported by the National Natural Science
Foundation of China (No: 10801109, 10926128 and 11047114), Science and Tech-
nology Research Projects of Hubei Provincial Department of Education (Q20091705)
and Young Talent Cultivation Projects of Guangdong (LYM09134).
References
1.
Chen, W.H., Lu, X.M.: Mean Square Exponential Stability of Uncertain Stochastic De-
layed Neural Networks. Physics Letters A 372, 1061-1069 (2008)
2.
Huang, C., Cao, J.: Almost Sure Exponential Stability of Stochastic Cellular Neural Net-
works with Unbounded Distributed Delays. Neurocomputing 72, 3352-3356 (2009)
3.
Huang, C., Cao, J.: On pth Moment Exponential Stability of Stochastic Cohen-Grossberg
Neural Networks with Time-varying Delays. Neurocomputing 73, 986-990 (2010)
4.
Huang, C., Chen, P., He, Y., Huang, L., Tan, W.: Almost Sure Exponential Stability of De-
layed Hopfield Neural Networks. Applied Mathematics Letters 21, 701-705 (2008)
5.
Huang, H., Feng, G.: Delay-dependent Stability for Uncertain Stochastic Neural Networks
with Time-varying Delay. Physica A 381, 93-103 (2007)
6.
Huang, C.X., He, Y.G., Huang, L.H., Zhu, W.J.: pth Moment Stability Analysis of Sto-
chastic Recurrent Neural Networks withTime-varying Delays. Information Sciences 178,
2194-2203 (2008)
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