Global stabilization of fractional-order memristive neural ...

$: Global stabilization of fractional-order memristive neural ...$
Global O(t−α) stabilization of fractional‑order memristive neural networks with time delaysLing Liu1, Ailong Wu1* and Xingguo Song2

BackgroundRecently, the fractional calculus serving the fractional-order models develops fast in both theoretical and application. The analysis about fractional-order models has attracted increasing attention cause of its promising applications in various areas of sci-ence and engineering (see Chen and Chen 2015; Chen et al. 2014; Liu et al. 2015; Liang et al. 2015; Li et al. 2015; Rakkiyappan et al. 2014, 2015b; Stamova 2014; Velmurugan and Rakkiyappan 2016; Velmurugan et al. 2016; Wang et al. 2014; Wu and Zeng 2016; Wu et al. 2016). Comparing with integer-order systems, fractional-order systems show the superiority of describing and modeling the real world or the practical problems such as anomalous diffusion, signal processing, fractal theory and continuum mechanics. Whereas, it is arduously to promote the development of research about fractional-order models for the absence of efficient mathematical tools. As mentioned by Chen and Chen (2016), Chen et al. (2014), some new and useful methods for the qualitative analysis of fractional-order models are very imperative.

On the other hand, memristor is a circuit element which was proposed by Chua (1971) and has been realized the prototype by Hewlett-Packard laboratory in Strukov et al. (2008) and Tour and He (2008). Different from classical resistors, memristor is a nonlin-ear resistor which owns non-uniqueness values. In addition, the memristor can manage

Abstract

This article is concerned with the global O(t−α) stabilization for a class of fractional-order memristive neural networks with time delays (FMDNNs). Two kinds of con-trol scheme (i.e., state feedback control law and output feedback control law) are employed to stabilize a class of FMDNNs. Several stabilization conditions in form of algebraic criteria are presented based on a new fractional-order Lyapunov function method and Leibniz rule. Some examples are given to substantiate the effectiveness of the presented theoretical results.

Keywords: Fractional-order systems, Memristive neural networks, Global O(t−α) stabilization

Mathematics Subject Classification: 37B25, 34A08

Open Access

© 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

RESEARCH

Liu et al. SpringerPlus (2016) 5:1034 DOI 10.1186/s40064‑016‑2374‑3

*Correspondence: [email protected] 1 College of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, ChinaFull list of author information is available at the end of the article

http://creativecommons.org/licenses/by/4.0/

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Page 2 of 22Liu et al. SpringerPlus (2016) 5:1034

and store a great quantity of information. For its excellent properties about memory, we can build a new model if the conventional resistors are replaced by the memristors in neural networks, which is called memristive neural networks. Some representative works studied on the properties of the memristive systems display its applicability in several interdisciplinary areas (see Bao and Zeng 2013; Guo et al. 2015; Wang et al. 2003; Wu et al. 2012; Wu and Zeng 2012; Wen and Zeng 2012; Zhao et al. 2015). From the description of memristive neural networks, combining memristors with infinite mem-ory is extremely interesting. An advantage of fractional-order systems in comparison to integer-order systems is that fractional-order systems can generate infinite memory. Therefore, merging the memristors into a class of fractional-order neural networks is pretty anticipated. Although stability analysis of fractional-order memristive or mem-ristor-based neural networks has been gradually carried out (see Chen et al. 2014, 2015; Rakkiyappan et al. 2014, 2015b; Velmurugan and Rakkiyappan 2016; Velmurugan et al. 2016), it is worth noting that fractional-order memristive neural networks can exhibit complicated dynamics or chaotic behaviors if the network’s parameters and time delays are appropriately specified.

Noticed that many static or dynamic control laws have been designed to stabilize nonlinear control systems, for instance, Chandrasekar and Rakkiyappan (2016), Chen et al. (2015), Guo et al. (2013), Huang et al. (2009), Lou et al. (2013), Mathiyalagan et al. (2015), Rakkiyappan et al. (2015a), Wu et al. (2016), Yang and Tong (2016). In allusion to different system structures and actual control requirements, lots of stabilization crite-ria are established, for example, periodic intermittent stabilization (Huang et al. 2009), robust stabilization (Yang and Tong 2016), finite-time stabilization (Zhang et al. 2016), impulsive stabilization (Chandrasekar and Rakkiyappan 2016; Huang 2010; Lou et al. 2013). Despite these fruitful achievements, some stabilization approaches can hardly be widely applied in practical problems due to high gain. In addition, an undeniable fact is that stabilization control schemes of fractional-order systems is little studied. Hence, it is necessary to investigate some appropriate controllers for stabilization of fractional-order systems.

Inspired by the above discussion, in this article, we will study the global O(t−α) stabi-lization problem for a class of fractional-order memristive neural networks with time delays. We first introduce the concepts about fractional calculation and global stabiliza-tion of fractional-order systems. Secondly, for exploring some simple useful controllers, linear state feedback control law and linear output feedback control law are designed to stabilize the fractional-order systems. In addition, stabilization criteria in form of alge-braic inequalities are derived by utilizing a new fractional Lyapunov method instead of classical Gronwall inequality. The conditions can be easily verified.

Fractional calculation and model descriptionFractional calculation concepts

First of all, some basics of fractional calculation are given which will be used in the later.

Definition 1 (Chen and Chen 2016) The fractional integral with fractional order α > 0 of function f(t) is defined as


where t ≥ t0, Ŵ(·) is the Gamma function, that is

Definition 2 (Chen and Chen 2016) The Riemann–Liouville derivative with fractional order α > 0 of function f(t) is defined as

where t ≥ t0, n− 1 < α < n, n is a positive integer. Moreover, when 0 < α < 1, that is

Definition 3 (Chen and Chen 2016) The Caputo derivative with fractional order α > 0 of function f ∈ Cn+1([t0,+∞),R) is defined as

where t ≥ t0, n− 1 < α < n, n is a positive integer. Moreover, when 0 < α < 1, that is

Lemma 1 (Chen and Chen 2016) If f ∈ C1([0,+∞),R), then the following properties hold:

(1) Ct0Dαt f (t) =

RLt0Dαt f (t)−

f (t0)Ŵ(1−α)

(t − t0)−α.

(2) If f(t) and ϑ and their all derivatives are continuous in [t0, t], then Leibniz rule for fractional differentiation can be expressed as follows:

where 0 < α < 1, n ≥ α,

and

RLt0D−αt f (t) =

1

Ŵ(α)

∫ t

t0

(t − s)α−1f (s)ds,

Ŵ(α) =

∫ ∞

0

sα−1e−sds.

RLt0Dαt f (t) =

1

Ŵ(n− α)

dn

dtn

∫ t

t0

f (s)

(t − s)α−n+1ds,

RLt0Dαt f (t) =

1

Ŵ(1− α)

d

dt

∫ t

t0

f (s)

(t − s)αds.

Ct0Dαt f (t) =

1

Ŵ(n− α)

∫ t

t0

f (n)(s)

(t − s)α−n+1ds,

Ct0Dαt f (t) =

1

Ŵ(1− α)

∫ t

t0

f′(s)

(t − s)αds.

RLt0Dαt (ϑ(t)f (t)) =

n∑

k=0

dkϑ(t)

dtk

(α

k

)RLt0Dα−kt f (t)− Rα

n ,

Rαn(t) =

(−1)n(t − α)n−α+1

n!Ŵ(−α)

∫ 1

0

∫ 1

0

Fα(t, ξ , η)dξdη,

Fα(t, ξ , η) = f (t0 + η(t − t0))ϑn+1(t0 + (t − t0)(ξ + η − ξη)),


Model description

Consider the fractional-order memristive neural networks with time delays (FMDNNs) described by the following fractional-order equations: for i = 1, 2, . . . , n,

where 0 < α < 1, n is the number of neurons in the networks, xi(t) is the state variable of the ith neuron, gj(·), fj(·) denotes the output of the jth unit at time t and t − τ (t), respectively, and gj(0) = fj(0) = 0. τ (t) corresponds to the transmission delay at time t and 0 ≤ τ (t) ≤ τ. ui(t) denotes the external input, aij(xj(t)) and bij(xj(t)) represent memristive weights, which are defined as:

for i, j = 1, 2, . . . , n, aij(±T j) = aij or aij , bij(±T j) = bij or bij , where the switching jumps Tj > 0, aij, aij, bij, and bij are constants.

Remark 1 Note that aij(xj(t)) and bij(xj(t)) are discontinuous in system (1), then the classical definition of solution for differential equations cannot be applied to (1). To deal with this issue, we introduce the concept of Filippov solution.

Definition 4 (Rakkiyappan et al. 2014) For system Ct0Dαt x(t) = g(x), 0 < α < 1, x ∈ R

n, with a discontinuous right-hand side, a set-valued map is defined as

where co[E] is the closure of convex hull of set E, B(x, δ) = {y : �y− x� ≤ δ}, and µ(N ) is a Lebesgue measure of set N. If x(t), t ∈ [t0,T ], is called the solution in Filippov sense of the Cauchy problem for system Ct0D

αt x(t) = g(x), 0 < α < 1, x ∈ R

n, with initial condi-tion x(t0) = x0, when it is absolutely continuous, and satisfies the differential inclusion as follows:

For FMDNNs (1), define the set-value maps

(α

k

)=

Ŵ(α + 1)

k!Ŵ(α − k + 1).

(1)

Ct0Dαt xi(t) = − xi(t)+

n∑

j=1

aij(xj(t))gj(xj(t))

+

n∑

j=1

bij(xj(t))fj(xj(t − τ (t)))+ ui(t),

(2)aij(xj(t)) =

{aij ,

∣∣xj(t)∣∣ > Tj ,

aij ,∣∣xj(t)

∣∣ < Tj ,bij(xj(t)) =

{bij ,

∣∣xj(t)∣∣ > Tj ,

bij ,∣∣xj(t)

∣∣ < Tj ,

ψ(x) =⋂

δ>0

⋂

µ(N )=0

co[g(B(x, δ)\N )],

Ct0Dαt x(t) ∈ ψ(x), for a.e. t ∈ [t0,T ].


for i, j = 1, 2, . . . , n, where co{aij , aij} denotes the closure of convex hull generated by real numbers aij and aij, co{bij , bij} denotes the closure of convex hull generated by real num-bers bij and bij.

Throughout this article we denote amij = max1≤i,j≤n{|aij|, |aij|}, bmij = max1≤i,j≤n

{|bij|, |bij|}. For n-dimensional vector v = (v1, v2, . . . , vn)T, the norm of vector v is

recorded as �v� =∑n

i=1 |vi|. Cτ := C([−τ , 0],R) is a Banach space of all continuous functions ϕ : [−τ , 0] → R. For ϕ ∈ Cτ, let �ϕ�C = sups∈[−τ ,0] �ϕ(s)�.

Throughout this article, let us suppose: the activation functions gi, fi, i = 1, 2, . . . , n, are global Lipschitz, that is, for all u, v ∈ R, there exist positive constants Gi, Fi such that

The objective of this article is to investigate the global O(t−α) stabilization problem for system (1). Therefore, the stabilization problem will be converted to find the suit con-troller ui(t) (i = 1, 2, . . . , n) such that zero solution of the closed-loop system of (1) is globally O(t−α) stable.

From the theories of differential inclusions and set-valued maps, the Filippov solution of FMDNNs (1) can be defined in the following form.

Definition 5 A function x(t) = (x1(t), x2(t), . . . , xn(t))T is said to be a Filippov solu-

tion of FMDNNs (1) on [0, T) with initial conditions x(s) = ϕ(s), s ∈ [−τ , 0], if x(t) is absolutely continuous on any compact interval of [0, T), and

for t ≥ t0, i = 1, 2, . . . , n. Or equivalently, for i, j = 1, 2, . . . , n, there exist γ aij (xj(t)) ∈ K (aij(xj(t))), γ b

ij (xj(t)) ∈ K (bij(xj(t))) such that

K (aij(xj(t))) =

aij ,��xj(t)

�� > Tj ,

co�aij , aij

�,

��xj(t)�� = Tj ,

aij ,��xj(t)

�� < Tj ,

K (bij(xj(t))) =

bij ,��xj(t)

�� > Tj ,

co�bij , bij

�,

��xj(t)�� = Tj ,

bij ,��xj(t)

�� < Tj ,

∣∣gi(u)− gi(v)∣∣ ≤ Gi|u− v|,

∣∣fi(u)− fi(v)∣∣ ≤ Fi|u− v|.

(3)

Ct0Dαt xi(t) ∈ −xi(t)+

n∑

j=1

K (aij(xj(t)))gj(xj(t))

+

n∑

j=1

K (bij(xj(t)))fj(xj(t − τ (t)))+ ui(t),

(4)

Ct0Dαt xi(t) = −xi(t)+

n∑

j=1

γ aij (xj(t))gj(xj(t))

+

n∑

j=1

γ bij (xj(t))fj(xj(t − τ (t)))+ ui(t).


Remark 2 Based on the definitions of Filippov solution and fractional-order differential inclusion, we know that FMDNNs (1) is equivalent to the fractional-order differential inclusion (3) in the Filippov framework.

Next, definitions of global O(t−α) stability and global O(t−α) stabilization are given.

Definition 6 [Global O(t−α) stability] The zero solution of FMDNNs (1), where ui(t) = 0, is said to be globally O(t−α) stable if there exists a positive constant M such that �x(t, t0,ϕ)� ≤ M�ϕ�CO(t−α) for any ϕ ∈ Cτ and t ≥ t0.

Definition 7 [Global O(t−α) stabilization] FMDNNs (1) is said to be globally O(t−α) stabilized if there exists an appropriate feedback control law such that the closed-loop system of (1) is globally O(t−α) stable.

Main resultsState feedback control law

Two kinds of linear controller about state feedback are given, i.e., the linear controller without or with time delays. Firstly, we propose the following state control rule without time delays:

for i = 1, 2, . . . , n.

Theorem 1 FMDNNs (1) with the state feedback control rule (5) can be achieved global O(t−α) stabilization for any ϕ ∈ Cτ if there exist a constant r > τ and n positive constants βi (i = 1, 2, . . . , n) such that

for all i = 1, 2, . . . , n.

Proof Define two Lyapunov functions as follows:

and let

for t ≥ t0.

(5)ui(t) =

n∑

j=1

pijxj(t),

(6)

n∑

j=1

βjpij ≤ βi

(1−

1+ α

rαŴ(2− α)

)−

n∑

j=1

βj

(amij Gj +

(r

r − τ

)α

bmij Fj

),

(7)

{W (t) = max

{|xi(t)|βi

, i = 1, 2, . . . , n},

V (t) = (t − t0 + r)αW (t),

(8)

{W (t) = sup−τ≤θ≤t W (θ),

V (t) = sup−τ≤θ≤t V (θ),


From Leibniz rule for fractional differentiation, we have

for t ≥ t0.By computing, it follows that

(9)

Ct0Dαt V (t) = RL

t0Dαt V (t)−

V (t0)

Ŵ(1− α)(t − t0)

−α

= RLt0Dαt

((t − t0 + r)αW (t)

)−

rαW (t0)

Ŵ(1− α)(t − t0)

−α

= (t − t0 + r)αRLt0 Dαt W (t)+

Ŵ(α + 1)

Ŵ(α)α(t − t0 + r)α−1RL

t0Dα−1t W (t)

+Ŵ(α + 1)

2Ŵ(α − 1)α(α − 1)(t − t0 + r)α−2RL

t0Dα−2t W (t)

− Rα2 (t)−

rαW (t0)

Ŵ(1− α)(t − t0)

−α

≤ (t − t0 + r)αCt0Dαt W (t)+ (t − t0 + r)α

W (t0)

Ŵ(1− α)(t − t0)

−α

+ α2(t − t0 + r)α−1RLt0Dα−1t W (t)

+α2(α − 1)2

2(t − t0 + r)α−2RL

t0Dα−2t W (t)−

rαW (t0)

Ŵ(1− α)(t − t0)

−α

≤ (t − t0 + r)αCt0Dαt W (t)+

[(t − t0 + r

t − t0

)α

−

(r

t − t0

)α] W (t0)

Ŵ(1− α)

+ α2(t − t0 + r)α−1RLt0Dα−1t W (t)

+α2(α − 1)2

2(t − t0 + r)α−2RL

t0Dα−2t W (t)

≤ (t − t0 + r)αCt0Dαt W (t)+ (1+ 2α)

V (t)

rαŴ(1− α)

+ α2(t − t0 + r)α−1RLt0Dα−1t W (t)

+α2(α − 1)2

2(t − t0 + r)α−2RL

t0Dα−2t W (t),

(10)

α2(t − t0 + r)α−1RLt0Dα−1t W (t) = α2(t − t0 + r)−(1−α)RL

t0D−(1−α)t W (t)

≤ α2(t − t0 + r)−(1−α) 1

Ŵ(1− α)W (t)

∫ t

t0

(t − s)−αds

≤ α2(t − t0 + r)−(1−α) (t − t0)1−α

(1− α)Ŵ(1− α)W (t)

≤ α2

(t − t0

t − t0 + r

)1−α 1

Ŵ(2− α)W (t)

≤α2

Ŵ(2− α)W (θ )

≤α2

rαŴ(2− α)V (t),


and

for t ≥ t0, W (t) = W (θ ), where θ ∈ [−τ , t].From (9) to (11), we have

It is obvious that there exists a k ∈ {1, 2, . . . , n} such that

for given t ≥ t0.From (7) and (8), we have

(11)

α2(α − 1)2

2(t − t0 + r)α−2RL

t0Dα−2t W (t)

≤α2(α − 1)2

2(t − t0 + r)α−2 1

Ŵ(2− α)

∫t

t0

(t − s)1−αW (s)ds

≤α2(α − 1)2

2(t − t0 + r)α−2 W (t)

Ŵ(2− α)

∫t

t0

(t − s)1−αds

≤α2(α − 1)2

2(t − t0 + r)α−2 (t − t0)

2−αW (t)

(2− α)Ŵ(2− α)

≤α2(α − 1)2

2

(t − t0

t − t0 + r

)2−αW (t)

(2− α)Ŵ(2− α)≤

α2(α − 1)2

2

W (t)

(2− α)Ŵ(2− α)

≤α2(α − 1)2

2rα(2− α)Ŵ(2− α)V (t) ≤

α2

rαŴ(2− α)V (t),

(12)

Ct0Dαt V (t) ≤ (t − t0 + r)αCt0D

αt W (t)+

2α2

rαŴ(2− α)V (t)+

(1+ 2α)V (t)

rαŴ(1− α)

= (t − t0 + r)αCt0Dαt W (t)+

1+ α

rαŴ(2− α)V (t).

W (t) =|xk(t)|

βk,

(13)

Ct0Dαt W (t) =

1

βk

Ct0Dαt |xk (t)| ≤

1

βksgn(xk (t))

Ct0Dαt xk (t)

≤1

βksgn(xk (t))

−xk (t)+

n�

j=1

γ akj(xj(t))gj(xj(t))+

n�

j=1

γ bkj(xj(t))fj(xj(t − τ (t)))+

n�

j=1

pkjxj(t)

≤1

βksgn(xk (t))

−xk (t)+

n�

j=1

amkjGj |xj(t)| +

n�

j=1

bmkj Fj |xj(t − τ (t))| +

n�

j=1

pkjxj(t)

≤ −|xk (t)|

βk+

1

βk

n�

j=1

βjamkjGj

|xj(t)|

βj+

1

βk

n�

j=1

βjbmkj Fj

|xj(t − τ (t))|

βj+

1

βk

n�

j=1

βjpkj|xj(t)|

βj

≤ −W (t)+1

βk

n�

j=1

βj(amkjGj + pkj)W (t)+

1

βk

n�

j=1

βjbmkj FjW (t − τ (t))

≤ −

1− 1

βk

n�

j=1

βj(amkjGj + pkj)

W (t)+

1

βk

n�

j=1

βjbmkj FjW (t − τ (t)).


And hence

where θ ∈ [−τ , t] such that V (t) = (t − t0 + θ + r)αW (t), when V (t) = V (t).From (12) and (14), we have

when V (t) = V (t).From (6), it follows that

for all t ≥ t0.On the basis of Definition 2 and Lemma 1, the following inequality holds

It yields

for t ≥ t0. Hence for i = 1, 2, . . . , n,

(14)

(t − t0 + r)αCt0Dαt W (t)

≤ −(t − t0 + r)α

1− 1

βk

n�

j=1

βj

�amkjGj + pkj

�W (t)

+ (t − t0 + r)α1

βk

n�

j=1

βjbmkjFjW (t − τ (t))

≤ −

1− 1

βk

n�

j=1

βj

�amkjGj + pkj

�V (t)+

(t − t0 + r)α

βk�t − t0 + r + θ

�αn�

j=1

βjbmkjFjV (t)

≤ −

1−

n�

j=1

βj

βk(amkjGj + pkj)−

�r

r + θ

�α n�

j=1

βj

βkbmkjFj

V (t)

≤ −

1−

n�

j=1

βj

βk

�amkjGj + pkj

�−

�r

r − τ

�α n�

j=1

βj

βkbmkjFj

V (t),

(15)

Ct0Dαt V (t) ≤ −

1−

n�

j=1

βj

βk(amkjGj + pkj)−

�r

r − τ

�α n�

j=1

βj

βkbmkj Fj

V (t)+

1+ α

rαŴ(2− α)V (t)

≤

−

1−

n�

j=1

βj

βk

�amkjGj + pkj

�−

�r

r − τ

�α n�

j=1

βj

βkbmkj Fj

+

1+ α

rαŴ(2− α)

V (t),

(16)Ct0Dαt V (t) ≤ 0,

1

Ŵ(1− α)

d

dt

∫ t

t0

V (s)

(t − s)αds ≤

V (t0)

Ŵ(1− α)(t − t0)

−α .

(17)V (t) ≤ V (t0),


where βmin = min{βi, i = 1, 2, . . . , n}, for t ≥ t0, which implies

where � = 1βmin

∑ni=1 βi. Therefore, FMDNNs (1) can be achieved global O(t−α) stabili-

zation under the designed control law (5). �

In the following, we propose the following state control rule with time delays:

for i = 1, 2, . . . , n.

Theorem 2 FMDNNs (1) with the state feedback control rule (19) can be achieved global O(t−α) stabilization for any ϕ ∈ Cτ if there exist a constant r > τ and n positive constants βi (i = 1, 2, . . . , n) such that

for all i = 1, 2, . . . , n.


and let

for t ≥ t0.Through Theorem 1, we have


(18)

|xi(t)| ≤ βiW (t)

= βiV (t)

(t − t0 + r)α≤ βi

V (t)

(t − t0 + r)α

≤ βiV (t0)

(t − t0 + r)α= βi

rαW (t0)

(t − t0 + r)α

≤βir

α�ϕ�C

βmin(t − t0 + r)α,

�x(t)� ≤�rα�ϕ�C

(t − t0 + r)α,

(19)ui(t) =

n∑

j=1

pijxj(t)+

n∑

j=1

qijxj(t − τ (t)),

(20)

n∑

j=1

βj

(pij +

(r

r − τ

)α

qij

)≤ βi

(1−

1+ α

rαŴ(2− α)

)−

n∑

j=1

βj

(amij Gj +

(r

r − τ

)α

bmij Fj

),

(21)

{W (t) = max

{|xi(t)|βi

, i = 1, 2, . . . , n},

V (t) = (t − t0 + r)αW (t),

(22)



(23)Ct0Dαt V (t) ≤ (t − t0 + r)αCt0D

αt W (t)+

1+ α




Hence

where θ ∈ [−τ , t] such that V (t) = (t + θ − t0 + r)αW (t), when V (t) = V (t).From (23) and (25), we have


for all t ≥ t0.On the basis of Definition 3, we get

W (t) =|xk(t)|

βk,

(24)

Ct0Dαt W (t) ≤

1

βksgn(xk (t))

Ct0Dαt xk (t)

≤1

βksgn(xk (t))

−xk (t)+

n�

j=1


n�

j=1

γ bkj(xj(t))fj(xj(t − τ(t)))

+

n�

j=1

pkjxj(t)+

n�

j=1

qkjxj(t − τ(t))

≤1

βksgn(xk (t))

−xk (t)+

n�

j=1

amkjGj|xj(t)| +

n�

j=1

bmkj Fj|xj(t − τ(t))|

+

n�

j=1

pkjxj(t)+

n�

j=1

qkjxj(t − τ(t))

≤ −

1− 1

βk

n�

j=1

βj(amkjGj + pkj)

W (t)+

1

βk

n�

j=1

βj

�bmkj Fj + qkj

�W (t).

(25)

(t−t0+r)αCt0Dαt W (t) ≤ −

1−

n�

j=1

βj

βk

�amkjGj + pkj +

�r

r − τ

�α�bmkjFj + qkj

��V (t),

(26)


1−

n�

j=1

βj

βk

�amkjGj + pkj +

�r

r − τ

�α�bmkj Fj + qkj

��V (t)+

1+ α

rαŴ(2− α)V (t)

≤

−

1−

n�

j=1

βj

βk

�amkjGj + pkj +

�r

r − τ

�α�bmkj Fj + qkj

��+

1+ α

rαŴ(2− α)

V (t),

(27)Ct0Dαt V (t) ≤ 0,

(28)V (t) ≤ V (t0),



where βmin = min{βi, i = 1, 2, . . . , n}, for t ≥ t0, it follows

where � = 1βmin

∑ni=1 βi. Therefore, FMDNNs can be achieved global O(t−α) stabiliza-

tion under the designed control law (19). �

Output feedback control law

Two kinds of linear controller about output feedback are given, i.e., the linear output feedback controller without or with time delays. Firstly, we propose the following output feedback control rule without time delays:

for i = 1, 2, . . . , n.

Theorem 3 FMDNNs (1) with the output feedback control rule (30) can be achieved global O(t−α) stabilization for any ϕ ∈ Cτ if there exist a constant r > τ and n positive constants βi (i = 1, 2, . . . , n) such that

for all i = 1, 2, . . . , n.


and let



(29)|xi(t)| ≤ βiW (t) ≤βir

α�ϕ�C



(t − t0 + r)α,

(30)ui(t) =

n∑

j=1

ωijgj(xj(t)),

(31)

n∑

j=1

βjGjωij ≤ βi

(1−

1+ α

rαŴ(2− α)

)−

n∑

j=1

βj

(amij Gj +

(r

r − τ

)α

bmij Fj

),

(32)

{W (t) = max

{|xi(t)|βi

, i = 1, 2, . . . , n},

V (t) = (t − t0 + r)αW (t),

(33)



(34)Ct0Dαt V (t) ≤ (t − t0 + r)αCt0D

αt W (t)+

1+ α




And hence

where θ ∈ [−τ , t] such that V (t) = (t − t0 + θ + r)αW (t), when V (t) = V (t).From (34) and (36), we have


for t ≥ t0.On the basis of Definition 3, we get

W (t) =|xk(t)|

βk,

(35)

Ct0Dαt W (t) =

1

βk

Ct0Dαt |xk(t)|

≤1

βksgn(xk(t))

Ct0Dαt xk(t)

≤1

βksgn(xk(t))

−xk(t)+

n�

j=1


n�

j=1

γ bkj(xj(t))fj(xj(t − τ (t)))

+

n�

j=1

ωkjgj(xj(t))

≤ −|xk(t)|

βk+

1

βk

n�

j=1

βjamkjGj

|xj(t)|

βj+

1

βk

n�

j=1

βjbmkj Fj

|xj(t − τ (t))|

βj

+1

βk

n�

j=1

βjωkjGj|xj(t)|

βj

≤ −

1− 1

βk

n�

j=1

βjGj

�amkj + ωkj

�W (t)+

1

βk

n�

j=1

βjbmkj FjW (t − τ (t)).

(36)

(t − t0 + r)αCt0Dαt W (t) ≤ −

1−

n�

j=1

βj

βk

�Gj

�amkj + ωkj

�+

�r

r − τ

�α

bmkjFj

�V (t),

(37)


1−

n�

j=1

βj

βk

�Gj

�amkj + ωkj

�+

�r

r − τ

�α

bmkjFj

�V (t)+

1+ α

rαŴ(2− α)V (t)

≤

−

1−

n�

j=1

βj

βk

�Gj

�amkj + ωkj

�+

�r

r − τ

�α

bmkjFj

�+

1+ α

rαŴ(2− α)

V (t),

(38)Ct0Dαt V (t) ≤ 0,



where βmin = min{βi, i = 1, 2, . . . , n}, for t ≥ t0, which implies

where � = 1βmin



In the following, we propose the following output feedback control rule with time delays:

for i = 1, 2, . . . , n.

Theorem 4 FMDNNs (1) with the output feedback control rule (41) can be achieved global O(t−α) stabilization for any ϕ ∈ Cτ if there exist a constant r > τ and n positive constants βi (i = 1, 2, . . . , n) such that

for all i = 1, 2, . . . , n.


and let



(39)V (t) ≤ V (t0),

(40)|xi(t)| ≤ βiW (t) ≤βir

α�ϕ�C



(t − t0 + r)α,

(41)ui(t) =

n∑

j=1

ωijgj(xj(t))+

n∑

j=1

ρij fj(xj(t − τ (t))),

(42)

n∑

j=1

βj

(Gjωij +

(r

r − τ

)α

Fjρij

)≤ βi

(1−

1+ α

rαŴ(2− α)

)−

n∑

j=1

βj

(amij Gj +

(r

r − τ

)α

bmij Fj

),

(43)

{W (t) = max

{|xi(t)|βi

, i = 1, 2, . . . , n},

V (t) = (t − t0 + r)αW (t),

(44)



(45)Ct0Dαt V (t) ≤ (t − t0 + r)αCt0D

αt W (t)+

1+ α


W (t) =|xk(t)|

βk,



Hence

where θ ∈ [−τ , t] such that V (t) = (t + θ − t0 + r)αW (t), when V (t) = V (t).From (45) and (47), we have

when V (t) = V (t).It follows that

for all t ≥ t0.On the basis of Definition 3, we get

for t ≥ t0. Hence i = 1, 2, . . . , n,

(46)

Ct0Dαt W (t) ≤

1

βksgn(xk (t))

Ct0Dαt0xk (t)

≤1

βksgn(xk (t))

−xk (t)+

n�

j=1


n�

j=1

γ bkj(xj(t))fj(xj(t − τ(t)))

+

n�

j=1

ωkjgj(xj(t))+

n�

j=1

ρkj fj(xj(t − τ(t)))

≤ −|xk (t)|

βk+

1

βk

n�

j=1

βjamkjGj

|xj(t)|

βj+

1

βk

n�

j=1

βjbmkj Fj

|xj(t − τ(t))|

βj

+1

βk

n�

j=1

βjωkjGj|xj(t)|

βj+

1

βk

n�

j=1

βjρkjFj|xj(t − τ(t))|

βj

≤ −[1−1

βk

n�

j=1

βjGj(amkj + ωkj)]W (t)+

1

βk

n�

j=1

βjFj(bmkj + ρkj)W (t).

(47)

(t − t0 + r)αCt0Dαt W (t) ≤ −

1−

n�

j=1

βj

βk

�Gj

�amkj + ωkj

�+

�r

r − τ

�α

Fj

�bmkj + ρkj

��V (t),

(48)


1−

n�

j=1

βj

βk

�Gj

�amkj + ωkj

�+

�r

r − τ

�α

Fj

�bmkj + ρkj

��V (t)+

1+ α

rαŴ(2− α)V (t)

≤

−

1−

n�

j=1

βj

βk

�Gj

�amkj + ωkj

�+

�r

r − τ

�α

Fj

�bmkj + ρkj

��+

1+ α

rαŴ(2− α)

V (t),

(49)Ct0Dαt V (t) ≤ 0,

(50)V (t) ≤ V (t0),

(51)|xi(t)| ≤ βiW (t) ≤βir

α�ϕ�C



where βmin = min{βi, i = 1, 2, . . . , n}, for t ≥ t0, it follows

where � = 1βmin



Remark 3 It needs to point out that fractional-order systems can be said rarely expo-nential stability. While, global Mittag–Leffler stability or global O(t−α) stability can be used to describe asymptotic stability of fractional-order systems. In consideration of the complex and rich nonlinear behaviors of fractional-order systems, especially, for the fractional-order systems with time delays, we employ global O(t−α) stabilization for a class of FMDNNs in Theorems 1–4.

Remark 4 As a useful tool, Lyapunov function method has been introduced to frac-tional-order systems by borrowing ideas from classical Lyapunov function method in integer-order systems. In Theorems 1–4, a class of new fractional Lyapunov functions have been established, which consist of two Lyapunov functions [i.e., time-invariant Lya-punov function W(t) and time-varying Lyapunov function V(t)]. For this structure of Lyapunov functions, we can regard the Caputo derivative of V(t) as two parts which can be estimated by means of Leibniz rule.

Numerical examplesIn this section, two numerical examples are given to show the effectiveness of the pro-posed theoretical results.

Example 1 Consider a two-dimensional FMDNNs as follows:

where α = 0.95, τ = 1, t0 = 0, gj(χ) = fj(χ) = tanh(χ) (j = 1, 2), and

It is obvious that we can get Gj = Fj = 1, j = 1, 2.

Figure 1 shows the results of time response of (52) without external controller, which implies that the state trajectory of (52) can not convergence to the origin.

Assume that there exist two positive constants β1 and β2 to satisfy


(t − t0 + r)α,

(52)

Ct0Dαt x1(t) = −x1(t)+ a11(x1(t))g1(x1(t))+ a12(x2(t))g2(x2(t))

+b11(x1(t))f1(x1(t − τ))+ b12(x2(t))f2(x2(t − τ))+ u1(t),Ct0Dαt x2(t) = −x2(t)+ a21(x1(t))g1(x1(t))+ a22(x2(t))g2(x2(t))

+b21(x1(t))f1(x1(t − τ))+ b22(x2(t))f2(x2(t − τ))+ u2(t),

a11(x1) =

{1.5, |x1| > 1,

2.0, |x1| < 1,a12(x2) =

{0.1, |x2| > 1,

0.2, |x2| < 1,a21(x1) =

{0.5, |x1| > 1,

0.4, |x1| < 1,

a22(x2) =

{1.8, |x2| > 1,

1.5, |x2| < 1,b11(x1) =

{−3.5, |x1| > 1,

−4.0, |x1| < 1,b12(x2) =

{−1.8, |x2| > 1,

−1.5, |x2| < 1,

b21(x1) =

{1.2, |x1| > 1,

1.0, |x1| < 1,b22(x2) =

{−1.8, |x2| > 1.

−1.5, |x2| < 1.


it follows that there exists a positive constant r such that

which implies the conditions of Theorem 1 hold.From Example 1, we have

then we can choose p11 = −5, p12 = −2, p21 = −2, p22 = −3, i.e., the state feedback controller without time delays can be designed as follows:

(53)

{β1p11 + β2p12 ≤ β1

(1−

(am11G1 + bm

11F1))

+ β2(am12G2 + bm

12F2),

β2p22 + β1p21 ≤ β2(1−

(am22G2 + bm

22F2))

+ β1(am21G1 + bm

21F1),

β1p11 + β2p12 ≤ β1

�1− 1+α

rαŴ(2−α)−

�am11G1 +

�r

r−τ

�αbm11F1

��− β2

�am12G2 +

�r

r−τ

�αbm12F2

�,

β2p22 + β1p21 ≤ β2

�1− 1+α

rαŴ(2−α)−

�am22G2 +

�r

r−τ

�αbm22F2

��− β1

�am21G1 +

�r

r−τ

�αbm21F1

�,

{β1(p11 + 5)+ β2(p12 + 2) ≤ 0.

β2(p22 + 2.6)+ β1(p21 + 1.7) ≤ 0,

0 5 10 15 20 25

−3

−2

−1

0

1

2

3

t

x 1

0 5 10 15 20 25−3

−2

−1

0

1

2

3

t

x 2

Fig. 1 Transient behavior of x1(t) and x2(t) for (52) without external controller


According to Theorem 1, system (52) can be achieved global O(t−α) stabilization. From Fig. 2, we can get that the state trajectory of the resulting closed-loop system of (52) with the designed control law (54) is globally O(t−α) stable.

Similarly, select the state feedback controller with time delays designed as follows:

Then it follows from Theorem 2 that system (52) can be achieved global O(t−α) stabiliza-tion. From Fig. 3, we can get that the state trajectory of the resulting closed-loop system of (52) with the designed control law (55) is globally O(t−α) stable.

Example 2 Consider an one-dimensional FMDNNs as follows:

(54)

{u1(t) = −5x1(t)− 2x2(t).

u2(t) = −2x1(t)− 3x2(t).

(55)

{u1(t) = −7x1(t)− 3x2(t)+ 0.5x1(t − 1)− 0.1x2(t − 1).u2(t) = −3x1(t)− 5x2(t)+ 0.1x1(t − 1)− 0.5x2(t − 1).

0 5 10 15 20−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

t

x 1

0 5 10 15 20 25

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

t

x 2

Fig. 2 Transient behavior of x1(t) and x2(t) for (52) with state feedback control rule u1(t) = −5x1(t)−

2x2(t), u2(t) = −2x1(t)− 3x2(t)


where α = 0.5, τ = 1, t0 = 0, g(χ) = sin(χ), f (χ) = tanh(χ), and

It is obvious that we can obtain G = F = 1.

Figure 4 shows the results of time response of (56) without external controller, which implies that the state trajectory of (56) can not convergence to the origin.

In order to apply Theorem 3, the following inequality needs to be satisfied

then we can choose ω = −5, i.e., the state feedback controller without time delays can be designed as follows:

(56)Ct0Dαt x(t) = −x(t)+ a(x(t))g(x(t))+ b(x(t))f (x(t − τ))+ u(t),

a(x) =

{1.0, |x| > 1,1.2, |x| < 1,

b(x) =

{−4.8, |x| > 1.−3.5, |x| < 1.

(57)ω + 5 ≤ 0,

(58)u(t) = −5 sin(x).

0 5 10 15−0.5

−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0.4

0.5

t

x 1

0 5 10 15−0.5

−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0.4

0.5

t

x 2

Fig. 3 Transient behavior of x1(t) and x2(t) for (52) with state feedback control rule u1(t) = −7x1(t)− 3x2(t)+ 0.5x1(t − 1)− 0.1x2(t − 1), u2(t) = −3x1(t)− 5x2(t)+ 0.1x1(t − 1)− 0.5x2(t − 1)


It follows from Theorem 3 that system (56) can be achieved global O(t−α) stabilization. From Fig. 5, we can get that the state trajectory of the resulting closed-loop system of (56) with the designed control law (58) is globally O(t−α) stable.

Similarly, select the output feedback controller with time delays designed as follows:

Then it follows from Theorem 4 that system (56) can be achieved global O(t−α) stabiliza-tion. From Fig. 6, we can get that the state trajectory of the resulting closed-loop system of (56) with the designed control law (59) is globally O(t−α) stable.

Concluding remarksIn this article, we exploit the global O(t−α) stabilization for a class of fractional-order memristive neural networks with time delays. The main theoretical results of this article are that the linear state feedback control law and the output feedback control law are

(59)u(t) = −7 sin(x)− tanh(x − 1).

0 5 10 15−3

−2

−1

0

1

2

3

t

x

Fig. 4 Transient behavior of x(t) for (56) without external controller

0 5 10 15−0.5

−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0.4

0.5

t

x

Fig. 5 Transient behavior of x(t) for (56) with output feedback control rule u(t) = −5 sin(x)


constructed to stabilize the fractional systems. In addition, some sufficient conditions ensuring to stabilize fractional-order systems are also given in terms of algebraic ine-qualities according to a new fractional Lyapunov function and a fractional-order differ-ential inequality skill. The article provides a novel way to construct a Lyapunov function and a new method to deal with fractional-order inequalities, which may be applied to discuss other properties or analyze other more complex systems such as the fractional-order form of the model explored in the literatures Chandrasekar and Rakkiyappan (2016), Lou et al. (2013), Shang (2014, 2015, 2016), Wang et al. (2003), Yang and Tong (2016) and so on. Future research will focus on these issues.Authors’ contributionsLL wrote the draft of this article. AW provided the idea of this article. XS offered the composition and polished English expression. All authors read and approved the final manuscript.

Author details1 College of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, China. 2 School of Mechanical Engi-neering, Southwest Jiaotong University, Chengdu 610031, China.

AcknowledgementsThe authors thank the Associate Editor and the reviewers for their suggestions which have been very useful to improve the quality of article. The work is supported by the Natural Science Foundation of China under Grant 61304057.

Competing interestsThe authors declare that they have no competing interests.

Received: 25 February 2016 Accepted: 19 May 2016

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0 5 10 15−0.5

−0.4

−0.3

−0.2

−0.1

0

0.1

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