Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 6398208 | https://doi.org/10.1155/2020/6398208

Hongyun Yan, Yuanhua Qiao, Lijuan Duan, Ling Zhang, "Global Mittag–Leffler Stabilization of Fractional-Order BAM Neural Networks with Linear State Feedback Controllers", Mathematical Problems in Engineering, vol. 2020, Article ID 6398208, 10 pages, 2020. https://doi.org/10.1155/2020/6398208

Global Mittag–Leffler Stabilization of Fractional-Order BAM Neural Networks with Linear State Feedback Controllers

Academic Editor: Jerzy Baranowski
Received11 Dec 2019
Revised24 Jun 2020
Accepted28 Jul 2020
Published17 Aug 2020

Abstract

In this paper, the global Mittag–Leffler stabilization of fractional-order BAM neural networks is investigated. First, a new lemma is proposed by using basic inequality to broaden the selection of Lyapunov function. Second, linear state feedback control strategies are designed to induce the stability of fractional-order BAM neural networks. Third, based on constructed Lyapunov function, generalized Gronwall-like inequality, and control strategies, several sufficient conditions for the global Mittag–Leffler stabilization of fractional-order BAM neural networks are established. Finally, a numerical simulation is given to demonstrate the effectiveness of our theoretical results.

1. Introduction

Bidirectional associative memory (BAM) neural networks are a type of extended unidirectional auto-associator of Hopfield neural networks. They are composed of two layers: the X-layer and the Y-layer, which can store and recall pattern pairs [1]. And they are widely applied in pattern recognition, signal processing, and associative memories. It is of great importance to investigate the dynamic stability behaviors of such networks to meet application requirements.

The fractional-order model is developed quickly because of its memory and genetic characteristics [25]. When the fractional order is in the interval , a new lemma for the Caputo fractional derivatives is proposed in [2]. Some properties of the Lyapunov direct method for noninteger order systems are presented in [3]. In [4], the authors extend Lyapunov direct method for noninteger order systems. In [5], the finite-time stability of fractional-order impulsive switched systems is considered. There are many research studies on fractional-order BAM neural networks [611]. A novel result about finite-time impulsive stability of fractional-order memristive BAM neural networks is obtained in [6]. Quasi-pinning synchronization and -exponential pinning stabilization for a class of fractional-order BAM neural networks with time-varying delays and discontinuous neuron activations are considered in [8]. In [10], sufficient conditions for the existence, uniqueness, and global Mittag–Leffler stability for the solutions of the fractional difference model of BAM neural networks are provided. In [11], based on Cauchy–Schwartz inequality and Burkholder–Davis–Gundy inequality, some sufficient conditions are derived to ensure the uniform stability of stochastic fractional-order memristor fuzzy BAM neural networks.

The definitions of Mittag–Leffler stability and generalized Mittag–Leffler stability are proposed in [12, 13]. Subsequently, some investigations focus on Mittag–Leffler stability [1421]. Global Mittag–Leffler stability and synchronization analysis of discrete fractional-order complex-valued neural networks with time delay are given in [14]. In [15], the global Mittag–Leffler stability of multiple equilibrium points for the impulsive fractional-order quaternion-valued neural networks is investigated by employing the Lyapunov method. In [16], sufficient conditions ensuring the existence, uniqueness, and global Mittag–Leffler stability of the solutions of the fractional-order coupled system on a network without strong connections are derived. In [17], a new criterion is proposed to ensure the Mittag–Leffler stability for fractional-order neural networks in the quaternion field. The finite-time Mittag–Leffler stability for fractional-order quaternion-valued memristive neural networks with impulsive effect is investigated in [20]. Researchers not only study stability but also introduce control strategies to improve the stability. Different controllers have been applied to postpone Hopf bifurcation and broaden the stability domain on fractional-order systems [2225]. Adaptive control approaches are adopted to induce Mittag–Leffler stabilization and synchronization for delayed fractional-order BAM neural networks in [26]. With linear and partial state feedback controls, global Mittag–Leffler stability of fractional-order BAM neural network is analyzed using Caputo fractional derivative and generalized Gronwall inequality [27]. In [28], state feedback stabilizing control and output feedback stabilizing control are designed for the stabilization of fractional-order memristive neural networks. However, the influence of whole state feedback controllers on stability of fractional-order BAM neural networks is not considered.

Motivated by the above discussion, we investigate the global Mittag–Leffler stability of fractional-order BAM neural networks with linear feedback controllers, including single and whole state feedback controllers. The main contributions include the following: First, a novel lemma is proposed using basic inequality to broaden the choice of the Lyapunov function. Second, linear state feedback controllers are designed to stabilize the systems. Third, some sufficient conditions for global Mittag–Leffler stability are given by using the fractional Lyapunov method and introducing feedback controllers. Finally, numerical simulations are performed to show the effect of different state feedback controllers on the selected system.

The paper is organized as follows. The preliminaries and the model descriptions are given in Section 2. Some sufficient conditions for global Mittag–Leffler stability of fractional-order BAM neural networks with two types of feedback controls are given in Section 3. In Section 4, a numerical simulation using the Adams-type forecast correction method is presented to illustrate the effectiveness of the theoretical results. Conclusions are given in Section 5.

2. Preliminaries and Model Description

In this section, some relevant definitions and lemmas about fractional calculus are introduced and the fractional-order BAM neural networks are described. Caputo fractional derivative is adopted.

Definition 1. (see [27]). The Caputo fractional derivative of order for a function is defined bywhere , is Euler’s gamma function, and .
Particularly, when ,

Definition 2. (see [27]). One-parameter Mittag–Leffler function is defined asand two-parameter Mittag–Leffler function is defined aswhere the real part of the complex number is , z and are both complex numbers, and is Euler’s gamma function. Obviously, , , and .
In this paper, we consider the fractional-order BAM neural networks given by the following fractional differential equations:where is the Caputo derivative of order , , , and are the neural states, and are the self-inhibitions, and are the synaptic connection strengths, and are the activation functions satisfying and , and and denote the external inputs.
To ensure the existence and uniqueness of solution of system (5), the following assumption is given.

Assumption 1. The neuron activation functions and satisfy Lipschitz condition with the Lipschitz constants and , i.e., for and , ,Now, we give the definition of the globally Mittag–Leffler stability for system (5) and some relevant lemmas.

Definition 3. (see [12]) (global Mittag–Leffler stability). Under the condition of and , the zero solution of system (5) is globally Mittag–Leffler stable if there exist two positive constants such that for any trajectories and of system (5) with initial values and , satisfyingwhere is p-norm, , , , , and is one-parameter Mittag–Leffler function.

Remark 1. If the equilibrium point is not at the origin, it can be shifted to the origin by coordinate transformation, so we just considered the case of zero equilibrium point.

Definition 4. (see [28]) (global Mittag–Leffler stabilization). System (5) is globally Mittag–Leffler stabilizable if there exists a suitable feedback control, such that closed-loop system (5) is globally Mittag–Leffler stable.

Lemma 1. (see [29]). If is a vector, where are continuous and differentiable functions, for all , and is a positive definite matrix, then for general quadratic form function , we have

Lemma 2. (see [27]) (generalized Gronwall-like inequality). Let be a continuous function on , if there exist constants and such that

Then,where and and are one-parameter Mittag–Leffler function and two-parameter Mittag–Leffler function, respectively.

Next, we propose a new lemma by applying basic inequality to broaden the choice of the Lyapunov function for analyzing the stability.

Lemma 3. For state vectors and , if there exist and two positive definite matrices , and all eigenvalues of and are greater than or equal to 1, the following inequality is satisfied:

Then,where , is one-parameter Mittag–Leffler function and and are the initial value.

Proof. By inequality (11) and basic inequality , we haveCombining inequalities (11) and (13), we obtainThen,

3. Main Results

In this section, linear state feedback controls are designed and some sufficient conditions are derived to ensure global Mittag–Leffler stability of fractional-order BAM neural networks.

3.1. A Single Linear State Feedback Control

The external inputs and in system (5), which only depend on a single linear state feedback control, are designed as follows:

Theorem 1. Ifis a negative definite matrix, where and are negative definite matrices and , then system (5) is globally Mittag–Leffler stable under designed control law (16).

Proof. Since is a negative definite matrix, there exist such that is a negative semidefinite matrix, where , , andConsider a Lyapunov function as follows:Using Lemma 1, we haveLetAccording to the conditions of Theorem 1, obviously . Combining (20) and (21), we obtainUsing Lemma 2, we obtainThat is,According to Lemma 3, we haveBy Definitions 3 and 4, system (5) is globally Mittag–Leffler stable under designed control law (16).

3.2. The Whole Linear State Feedback Control

Meanwhile, we consider the following the external inputs and in system (5) that depend on the whole linear state feedback control:

In the proof of Theorem 1, it is a little strict to construct a negative definite matrix. Now, we will explore the stability of system (5) by establishing a nonzero matrix with zero diagonal elements and control law (26).

Theorem 2. If the conditionsare satisfied and is a nonzero matrix with zero diagonal elements, where and

is the largest eigenvalue of matrix ; then, system (5) is globally Mittag–Leffler stable under designed control law (26).

Proof. Since the diagonal elements of are 0, the trace of N is 0, and obviously, .
Consider the following Lyapunov function:By Lemma 1, we obtaindue toSubstituting (31) into (30), we havewhereThat is,Using Lemma 2, we obtainThat is,According to Lemma 3, we haveBy Definitions 3 and 4, system (5) is globally Mittag–Leffler stable under designed control law (26).

Remark 2. In the proof of Theorems 1 and 2, by constructing a negative definite matrix or a nonzero matrix with zero diagonal elements, we enlarge the inequality and have . Then, based on Lemma 2, the condition of Lemma 3 is satisfied , where and of Lemma 3 are set to be unit matrices. Finally, using Lemma 3 and definition of Mittag–Leffler stability, we determine that system (5) is globally Mittag–Leffler stable under different control laws.

4. Numerical Simulation

In this section, a numerical example is given to show the effectiveness of our proposed theoretical results by Adama–Bashforth–Moulton predictor-corrector algorithm [30].

Consider the following four-dimensional fractional-order BAM neural networks:with the initial condition , where , , and , .

As depicted in Figure 1, the state trajectories of system (38) without external controllers and cannot converge to the origin.

Based on selected activation functions and , we have Lipschitz constants . For single state feedback controller (16), setting , , , and , we obtain a negative definite matrix :

Therefore, it follows from Theorem 1 that system (38) can achieve global Mittag–Leffler stability under the designed single linear state feedback control laws , , , and . Figure 2 shows state trajectories of system (38) with the designed single linear state feedback control law. As shown in Figure 2, state trajectories of system (38) converge to the origin.

By Theorem 2, we obtain a nonzero matrix with zero diagonal elements and calculate the maximal eigenvalue of matrix :

For whole linear state feedback control law (26), let and, the conditions of Theorem 2 are satisfied as follows:

Therefore, system (38) is globally Mittag–Leffler stable under the whole state feedback control law. Figure 3 shows state trajectories of system (38) with the designed whole linear state feedback control law.

5. Conclusion

In this paper, the global Mittag–Leffler stabilization problem for a class of fractional-order BAM neural networks is analyzed. Linear state feedback control is designed to induce the stability of fractional-order BAM neural networks, including a single linear state feedback control and the whole linear state feedback control. A novel lemma is proposed to broaden the selection of Lyapunov function. Some sufficient conditions of global Mittag–Leffler stability are given by using fractional Lyapunov stability theory, generalized Gronwall-like inequality, and the designed controls. In addition, a numerical example is given to show the influence of different state feedback controllers on the selected system. In the future research, we will try to investigate the effect of external disturbances on the stability of fractional-order BAM neural networks under linear state feedback controls. And we will explore in depth the stability of incommensurating fractional-order BAM neural networks with time delay.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research was supported by the Beijing Municipal Natural Science Foundation (No. 4202025) and partially sponsored by the National Natural Science Foundation of China (No. 61672070) and Beijing Municipal Commission of Education (KZ201910005008).

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Copyright © 2020 Hongyun Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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