In this paper, we put forward an interesting fixed-time (FXT) stability lemma, which is based on a whole new judging condition, and the minimum upper bound for the stability start time is obtained. In the new FXT stability lemma, the mathematical relation between the upper bound of the stability start time and the system parameters is very simple, and the judgment condition only involves two system parameters. To indicate the usability of the new FXT stability lemma, we utilize it to study the FXT stability of a bidirectional associative memory neural network (BAMNN) with bounded perturbations via sliding mode control. To match the developed FXT stability lemma, novel sliding mode state variables and a two-layer sliding mode controller are designed. According to the developed FXT stability lemma, the perturbed BAMNN can achieve FXT stability under the devised sliding mode controller. The upper bound of the stability start time can be calculated easily by virtue of the control parameters, and the sufficient conditions guaranteeing that the perturbed BAMNN can achieve FXT stability have also been derived. Last, we provide some confirmatory simulations.

1.
B.
Kosko
, “
Bidirectional associative memories
,”
IEEE Trans. Syst. Man Cybern.
18
(
1
),
49
60
(
1988
).
2.
C.
Chen
,
L.
Li
,
H.
Peng
, and
Y.
Yang
, “
Adaptive synchronization of memristor-based BAM neural networks with mixed delays
,”
Appl. Math. Comput.
322
,
100
110
(
2018
).
3.
C.
Chen
,
L.
Li
,
H.
Peng
, and
Y.
Yang
, “
Fixed-time synchronization of memristor-based BAM neural networks with time-varying discrete delay
,”
Neural Netw.
96
,
47
54
(
2017
).
4.
P.
Balasubramaniam
,
M.
Kalpana
, and
R.
Rakkiyappan
, “
Global asymptotic stability of BAM fuzzy cellular neural networks with time delay in the leakage term, discrete and unbounded distributed delays
,”
Math. Comput. Model.
53
,
839
853
(
2011
).
5.
S.
Lakshmanan
,
J.
Park
,
T.
Lee
,
H.
Jung
, and
R.
Rakkiyappan
, “
Stability criteria for BAM neural networks with leakage delays and probabilistic time-varying delays
,”
Appl. Math. Comput.
219
,
9408
9423
(
2013
).
6.
H.
Liu
,
O.
Yan
,
J.
Hu
, and
T.
Liu
, “
Delay-dependent stability analysis for continuous-time BAM neural networks with Markovian jumping parameters
,”
Neural Netw.
23
(
3
),
315
321
(
2010
).
7.
Y.
Li
, “
Global exponential stability of BAM neural networks with delays and impulses
,”
Chaos, Solitons and Fractals
24
(
1
),
279
285
(
2005
).
8.
Q.
Zhou
, “
Global exponential stability of BAM neural networks with distributed delays and impulses
,”
Nonlinear Anal.: Real World Appl.
10
(
1
),
144
153
(
2009
).
9.
Y.
Li
and
X.
Fan
, “
Existence and globally exponential stability of almost periodic solution for Cohen-Grossberg BAM neural networks with variable coefficients
,”
Appl. Math. Model.
33
(
4
),
2114
2120
(
2009
).
10.
X.
Liu
,
N.
Jiang
,
J.
Cao
,
S.
Wang
, and
Z.
Wang
, “
Finite-time stochastic stabilization for BAM neural networks with uncertainties
,”
J. Franklin Inst.
350
(
8
),
2109
2123
(
2013
).
11.
C.
Rajivganthi
,
F.
Rihan
,
S.
Lakshmanan
, and
P.
Muthukumar
, “
Finite-time stability analysis for fractional-order Cohen-Grossberg BAM neural networks with time delays
,”
Neural Comput. Appl.
29
,
1309
1320
(
2018
).
12.
F.
Du
and
J.
Lu
, “
New approach to finite-time stability for fractional-order BAM neural networks with discrete and distributed delays
,”
Chaos, Solitons Fractals
151
,
111225
(
2021
).
13.
H.
Li
,
C.
Li
,
T.
Huang
, and
W.
Zhang
, “
Fixed-time stabilization of impulsive Cohen-Grossberg BAM neural networks
,”
Neural Netw.
98
,
203
211
(
2018
).
14.
C.
Aouiti
,
X.
Li
, and
F.
Miaadi
, “
A new LMI approach to finite and fixed time stabilization of high-order class of BAM neural networks with time-varying delays
,”
Neural Process. Lett.
50
(
1
),
815
838
(
2018
).
15.
A.
Polyakov
, “
Nonlinear feedback design for fixed-time stabilization of linear control systems
,”
IEEE Trans. Autom. Control
57
,
2106
2110
(
2012
).
16.
C.
Hu
,
J.
Yu
,
Z.
Chen
,
H.
Jiang
, and
T.
Huang
, “
Fixed-time stability of dynamical systems and fixed-time synchronization of coupled discontinuous neural networks
,”
Neural Netw.
89
,
74
83
(
2017
).
17.
W.
Lu
,
X.
Liu
, and
T.
Chen
, “
A note on finite-time and fixed-time stability
,”
Neural Netw.
81
,
11
15
(
2016
).
18.
C.
Chen
,
L.
Li
,
H.
Peng
,
Y.
Yang
,
L.
Mi
, and
H.
Zhao
, “
A new fixed-time stability theorem and its application to the fixed-time synchronization of neural networks
,”
Neural Netw.
123
,
412
419
(
2020
).
19.
L.
Lin
,
P.
Wu
,
Y.
Chen
, and
B.
He
, “
Enhancing the settling time estimation of fixed-time stability and applying it to the predefined-time synchronization of delayed memristive neural networks with external unknown disturbance
,”
Chaos
30
,
083110
(
2020
).
20.
S.
Parsegov
,
A.
Polyakov
, and
P.
Shcherbakov
, “
Nonlinear fixed-time control protocol for uniform allocation of agents on a segment
,”
Dokl. Math.
87
,
133
136
(
2013
).
21.
R.
Aldana-López
,
D.
Gómez-Gutiérrez
,
E.
Jiménez-Rodríguez
,
J.
Sánchez-Torres
, and
M.
Defoort
, “
Enhancing the settling time estimation of a class of fixed-time stable systems
,”
Int. J. Robust Nonlinear Control
29
,
4135
4148
(
2019
).
22.
M.
Roopaei
and
M.
Zolghadri Jahromi
, “
Synchronization of two different chaotic systems using novel adaptive fuzzy sliding mode control
,”
Chaos
18
,
033133
(
2008
).
23.
J.
Xiao
,
Z.
Zeng
,
A.
Wu
, and
S.
Wen
, “
Fixed-time synchronization of delayed Cohen-Grossberg neural networks based on a novel sliding mode
,”
Neural Netw.
128
,
1
12
(
2020
).
24.
C.
Aouiti
,
Q.
Hui
,
H.
Jallouli
, and
E.
Moulay
, “
Sliding mode control-based fixed-time stabilization and synchronization of inertial neural networks with time-varying delays
,”
Neural Comput. Appl.
33
,
11555
11572
(
2021
).
25.
L.
Wang
,
Z.
Zeng
, and
M.
Ge
, “
A disturbance rejection framework for finite-time and fixed-time stabilization of delayed memristive neural networks
,”
IEEE Trans. Syst. Man Cybern. Syst.
51
(
2
),
905
915
(
2021
).
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