Fractional-order BAM Research Articles

Synchronization of multi-links impulsive fractional-order complex networks via feedback control based on discrete-time state observations

This paper addresses the issue of global Mittag-Leffler synchronization of multi-links impulsive fractional-order complex networks (MIFCNs). A class of feedback control based on discrete-time state observations is first adopted for synchronization of MIFCNs. By combining Lyapunov method with graph-theoretic approach, a synchronization criterion for MIFCNs is derived. In particular, this synchronization criterion is related to the order of fractional derivatives, the parameters of control and the topological structure of networks. Furthermore, as an application of our theoretical results, synchronization of multi-links impulsive fractional-order BAM neural networks is studied in detail and a synchronization criterion is also provided. Finally, a numerical example is presented to illustrate the validity and feasibility of theoretical results.

Comments on “Influence of Time Delay on Bifurcation in Fractional Order BAM Neural Networks With Four Delays”

Mathematical modeling is a tool that has a lot of applications in applied sciences. This has driven extensive investigations and the development of different methods in applied mathematics. Recently, neural networks have attracted a significant attention in various research areas owing to their promising application in signal and image processing, optimization solvers, intelligent control, quadratic optimization, and automatic control. In a recent paper <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , the authors discussed that the time delay has an important influence on the stability and Hopf bifurcation of the involved networks. The derived results in this paper are new and will play a key role in the future optimization of networks and the improvement of human life. However, the mathematical modeling discussed in <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> contains a wrong calculation in the early part of the paper related to the Hopf bifurcation, which is subsequently carried out throughout the manuscript. The purpose of this comment is to point out the calculation error in <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> and provide the correct calculation.

Quasi-pinning synchronization and stabilization of fractional order BAM neural networks with delays and discontinuous neuron activations

Abstract This manuscript concerns quasi-pinning synchronization and β-exponential pinning stabilization for a class of fractional order BAM neural networks with time-varying delays and discontinuous neuron activations (FBAMNNDDAs). Firstly, under the framework of Filippov solution and fractional-order differential inclusions analysis for the initial value problem of FBAMNNDDAs is presented. Secondly, two kinds of novel pinning controllers according to pinning control technique are designed. By means of fractional order Lyapunov method and designed pinning control strategy, the sufficient criteria is given first to ensure the quasi-synchronization for the dynamic behavior of FBAMNNDDAs. Furthermore, the error bound of pinning synchronization is explicitly evaluated. Thirdly, via Kakutani s fixed point theorem of set-valued map analysis, Razumikhin condition, and a nonlinear pinning controller, the existence and β-exponential stabilization of FBAMNNDDAs equilibrium point is obtained in the voice of linear matrix inequality (LMI) technique. Fourthly, based on as well as Mittag-Leffler function and growth condition, the global existence of a solution in the Filippov sense of such system is guaranteed with detailed proof. At last, a numerical example with computer simulations are performed to illustrate the effectiveness of proposed theoretical consequences.

New approach to global Mittag-Leffler synchronization problem of fractional-order quaternion-valued BAM neural networks based on a new inequality

In this paper, a novel kind of neural networks named fractional-order quaternion-valued bidirectional associative memory neural networks (FQVBAMNNs) is formulated. On one hand, applying Hamilton rules in quaternion multiplication which is essentially non-commutative, the system of FQVBAMNNs is separated into eight fractional-order real-valued systems. Meanwhile, the activation functions are considered to be quaternion-valued linear threshold ones which help to reduce the unnecessary computational complexity. On the other hand, based on fractional-order Lyapunov technology, a new fractional-order derivative inequality is established. Mainly by employing the new inequality technique, constructing three novel Lyapunov–Krasovskii functionals (LKFs) and designing simple linear controllers, the global Mittag-Leffler synchronization problems are investigated and the corresponding criteria are acquired for the system of FQVBAMNNs and its special cases such as fractional-order complex-valued BAM neural networks (FCVBAMNNs) and fractional-order real-valued BAM neural networks (FRVBAMNNs), respectively. Finally, two numerical examples are given to show the effectiveness and availability of the proposed results.

Global Mittag-Leffler stability analysis of impulsive fractional-order complex-valued BAM neural networks with time varying delays

This paper investigates the stability of impulsive fractional-order complex-valued BAM neural networks with time varying delays. As the extension of fractional-order real-valued BAM neural networks, fractional-order complex-valued BAM neural networks have complex-valued states, synaptic weights, and the activation functions. Two different kinds of activation functions are considered, along with popular bounded and Lipschitz-kind activation functions. By using Lyapunov function and Homomorphic mapping theorem, sufficient conditions for the existence of unique equilibrium and global asymptotic stability of complex-valued systems are derived. In derivation we separated nonlinear complex-valued activation functions into real and imaginary parts. Moreover, Mittag-Leffler stability for BAM neural networks(BAMNNs) have been proposed when the nonlinear complex activation functions are bounded. Simulation results are presented to prove the efficiency of the obtained methods.

Dissipativity Analysis of Memristor-Based Fractional-Order Hybrid BAM Neural Networks with Time Delays

Abstract A new class of memristor-based time-delay fractional-order hybrid BAM neural networks has been put forward. The contraction mapping principle has been adopted to verify the existence and uniqueness of the equilibrium point of the addressed neural networks. By virtue of fractional Halanay inequality and fractional comparison principle, not only the dissipativity has been analyzed, but also a globally attractive set of the new model has been formulated clearly. Numerical simulation is presented to illustrate the feasibility and validity of our theoretical results.

Non-fragile state estimation for fractional-order delayed memristive BAM neural networks

This paper deals with the non-fragile state estimation problem for a class of fractional-order memristive BAM neural networks (FMBAMNNs) with and without time delays for the first time. By means of a novel transformation and interval matrix approach, non-fragile estimators are designed and parameter mismatch problem is averted. Sufficient criteria are established to ascertain the error system is asymptotically stable based on fractional-order Lyapunov functionals and linear matrix inequalities (LMIs). Two examples are put forward to show the effectiveness of the obtained results.

Global stability analysis of fractional-order fuzzy BAM neural networks with time delay and impulsive effects

In this paper, the impulsive effects on the stability equilibrium solution for Riemann–Liouville fractional-order fuzzy BAM neural networks with time delay are investigated. Firstly, some sufficient conditions are derived for assuring the global asymptotic stability of the equilibrium point of the system is studied by applying the fractional Barbalat’s lemma, Lyapunov stability theorem and inequality scaling skills. Secondly, the existence and uniqueness of the equilibrium point of the system are analyzed by using the corresponding property of contraction mapping principle. Two different Riemann–Liouville fractional order derivatives β and α between the U-layer and V- layer are taken into account coexistent. Furthermore, a numerical example is given to verify the validity and feasibility of the obtained results.

Different impulsive effects on synchronization of fractional-order memristive BAM neural networks

This paper considered exponential synchronization in fractional-order memristive BAM neural networks (FMBAMNNs) with time delay via switching jumps mismatch. Exponential function is introduced for studying fractional-order differential system. According to double-layer structure of FMBAMNNs, two controllers are designed for the response FMBAMNNs. Particularly, more wide ranges of impulsive effects, which are affected by fractional-order $$\alpha $$ , are discussed in detail. One case is that the impulsive effect contributes to system convergence, and the other is that the impulsive effect destroys the system convergence. Based on the fractional stability theory and the definition of average impulsive interval, several criteria for achieving synchronization of FMBAMNNs are established. For different impulsive effects, the rate of convergence is precisely expressed. Finally, numerical examples verify the validity of the theoretical results.

Global Mittag-Leffler Synchronization for Fractional-Order BAM Neural Networks with Impulses and Multiple Variable Delays via Delayed-Feedback Control Strategy

This paper is concerned with the global Mittag-Leffler synchronization schemes for the Caputo type fractional-order BAM neural networks with multiple time-varying delays and impulsive effects. Based on the delayed-feedback control strategy and Lyapunov functional approach, the sufficient conditions are established to ensure the global Mittag-Leffler synchronization, which are described as the algebraic inequalities associated with the network parameters. The control gain constants can be searched in a wider range following the proposed synchronization conditions. The obtained results are more general and less conservative. A numerical example is also presented to illustrate the feasibility and effectiveness of the theoretical results based on the modified predictor–corrector algorithm.

Finite-time stability of fractional order BAM neural networks with time delay

In this work, we discuss the existence and finite-time stability conditions of equilibrium point for fractional- order BAM neural networks with time delay. Some sufficient conditions by Bellman-Gronwall inequality and contraction mapping. An example is given to illustrate the effectiveness of the results.

Existence and Globally Asymptotic Stability of Equilibrium Solution for Fractional-Order Hybrid BAM Neural Networks with Distributed Delays and Impulses

This paper investigates the existence and globally asymptotic stability of equilibrium solution for Riemann-Liouville fractional-order hybrid BAM neural networks with distributed delays and impulses. The factors of such network systems including the distributed delays, impulsive effects, and two different fractional-order derivatives between the U-layer and V-layer are taken into account synchronously. Based on the contraction mapping principle, the sufficient conditions are derived to ensure the existence and uniqueness of the equilibrium solution for such network systems. By constructing a novel Lyapunov functional composed of fractional integral and definite integral terms, the globally asymptotic stability criteria of the equilibrium solution are obtained, which are dependent on the order of fractional derivative and network parameters. The advantage of our constructed method is that one may directly calculate integer-order derivative of the Lyapunov functional. A numerical example is also presented to show the validity and feasibility of the theoretical results.

Finite-time Mittag-Leffler synchronization of fractional-order memristive BAM neural networks with time delays

This paper investigates the finite-time synchronization of delayed fractional-order memristive bidirectional associative memory neural networks (FMBAMNNs). Based on Lyapunov theory, fractional-order differential inequalities, norm properties and linear feedback controller, the separated criteria are obtained to ensure the finite-time synchronization of studied FMBAMNNs with different fractional order α, respectively. Moreover, the derived criteria are easily checked and they contribute to the reduction of amount of calculation. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results.

Global asymptotic stability of impulsive fractional-order BAM neural networks with time delay

In this paper, we study the global asymptotic stability of fractional-order BAM neural networks. We take both time delay and impulsive effects into consideration. Based on Lyapunov stability theorem, fractional Barbalat's lemma and Razumikhin-type stability theorem, some stability conditions that are independent of the form of specific delays can be obtained. At last, two illustrative examples are given to show the independence of the obtained two main results and to show the effectiveness of the obtained results.

Existence and Stability Analysis of Fractional Order BAM Neural Networks with a Time Delay

Based on the theory of fractional calculus, the contraction mapping principle, Krasnoselskii fixed point theorem and the inequality technique, a class of Caputo fractional-order BAM neural networks with delays in the leakage terms is investigated in this paper. Some new sufficient conditions are established to guarantee the existence and uniqueness of the nontrivial solution. Moreover, uniform stability of such networks is proposed in fixed time intervals. Finally, an illustrative example is also given to demonstrate the effectiveness of the obtained results.

Uniform Stability Analysis of Fractional-Order BAM Neural Networks with Delays in the Leakage Terms

A class of fractional-order BAM neural networks with delays in the leakage terms is considered. By using inequality technique and analysis method, several delay-dependent sufficient conditions are established to ensure the uniform stability of such networks. Moreover, the sufficient conditions guaranteeing the existence, uniqueness, and stability of the equilibrium point are also obtained. In addition, three simulation examples are given to demonstrate the effectiveness of the obtained results.

Finite-Time Stability of Fractional-Order BAM Neural Networks with Distributed Delay

Based on the theory of fractional calculus, the generalized Gronwall inequality and estimates of mittag-Leffer functions, the finite-time stability of Caputo fractional-order BAM neural networks with distributed delay is investigated in this paper. An illustrative example is also given to demonstrate the effectiveness of the obtained result.