High-order Hopfield Research Articles

Anti-periodic Solutions for Quaternion-Valued High-Order Hopfield Neural Networks with Time-Varying Delays

In this paper, quaternion-valued high-order Hopfield neural networks (QVHHNNs) with time-varying delays are considered. Theoretically, a QVHHNN can be separated into four real-valued systems, forming an equivalent real-valued system. By using a novel continuation theorem of coincidence degree theory and constructing an appropriate Lyapunov function, some sufficient conditions are derived to guarantee the existence and global exponential stability of anti-periodic solutions for QVHHNN, which are new and complement previously known results.

Almost automorphic synchronization of quaternion-valued high-order Hopfield neural networks with time-varying and distributed delays

AbstractIn this paper, we consider the problem of the almost automorphic synchronization of quaternion-valued high-order Hopfield neural networks (QVHHNNs) with time-varying and distributed delays. Firstly, to avoid the non-commutativity of quaternion multiplication, we decompose QVHHNNs into an equivalent real-valued system. Secondly, we use the Banach fixed point theorem to obtain the existence of almost automorphic solutions of QVHHNNs. Thirdly, by designing a novel state-feedback controller and constructing suitable Lyapunov functions, we obtain that the drive-response structure of QVHHNNs with almost automorphic coefficients can realize the exponential synchronization. Our results are completely new. Finally, a numerical example is given to illustrate the feasibility of our results.

Piecewise asymptotically almost automorphic solutions for impulsive non-autonomous high-order Hopfield neural networks with mixed delays

This paper is concerned with an impulsive non-autonomous high-order Hopfield neural network with mixed delays. Under proper conditions, we studied the existence, the uniqueness and the global exponential stability of asymptotic almost automorphic solutions for the suggested system. Our method was mainly based on the Banach’s fixed-point theorem and the generalized Gronwall–Bellman inequality. Moreover, four examples are presented to demonstrate the effectiveness of the proposed findings.

Dynamics and oscillations of generalized high-order Hopfield neural networks with mixed delays

Abstract Existence and uniqueness of pseudo almost automorphic solutions for a class of high-order Hopfield neural networks are established by employing a suitable fixed point theorem and differential inequality. Moreover, the attractivity and global exponential stability of the pseudo almost automorphic solution are also considered for the system. Two Numerical examples with graphical illustrations are given to illuminate our main results.

Almost automorphic solution for neutral type high-order Hopfield BAM neural networks with time-varying leakage delays on time scales

In this paper, by applying the exponential dichotomy theory on time scales, Banach contraction mapping principle and differential inequality analysis techniques, we establish some sufficient conditions for the existence and global exponential stability of almost automorphic solution for neutral type high-order Hopfield bidirectional associative memory (BAM) neural networks with time-varying leakage delays on time scales. In the addressed system, we not only consider the effects of the first-order neutral terms on networks, but also investigate the influences of the second-order neutral terms on the networks. High-order Hopfield BAM neural networks with continuously distributed leakage delays on time scales are also considered. Our results are completely new even if time scale T=R or T=Z and complementary to the previously existing results. Finally, three examples with numerical simulations are presented to illustrate the feasibility of our proposed theoretical results.

Finite time boundedness of neutral high-order Hopfield neural networks with time delay in the leakage term and mixed time delays

This article deals with the finite time boundedness (FTB) and FTB-stabilization problem for a general class of neutral high-order Hopfield neural networks (NHOHNNs) with time delay in the leakage term and mixed time delays. The mixed time delays consist of both discrete time-varying delays and infinite distributed delays. By using the topological degree theory, sufficient conditions are established to prove the existence of equilibrium points. Then, the Lyapunov–Krasovskii functional (LKF) method is used to prove sufficient conditions for the FTB. These conditions are in the form of linear matrix inequalities (LMIs) and can be numerically checked. Furthermore, a state feedback control is constructed to solve the FTB-stabilization problem. Finally, some numerical examples are presented to show the effectiveness of our main results.

Stability and Exponential Synchronization of High-Order Hopfield Neural Networks with Mixed Delays

ABSTRACTThis paper investigates the problems of stability and synchronization for high-order recurrent neural networks with mixed delays. Firstly, we establish sufficient conditions to ensure the asymptotic stability and then the exponential synchronization. Furthermore, our results are applied to two chosen systems to demonstrate the effectiveness of the obtained theoretical results.

Periodic attractor for reaction–diffusion high-order Hopfield neural networks with time-varying delays

This paper is concerned with a class of reaction–diffusion high-order Hopfield neural networks with time-varying delays subject to the Dirichlet boundary condition in a bounded domain. Easily verifiable delay-independent criteria are established to ensure the existence of periodic mild solutions, and the global exponential stability of the periodic mild solutions is also discussed by using the exponential dissipation property of semigroup of operators. The obtained results are easy to check and they effectually complement previously known results. A numerical example is given to show the effectiveness of theoretical results.

Pseudo Almost Periodic Solutions for High-Order Hopfield Neural Networks with Time-Varying Leakage Delays

In this paper, high-order Hopfield neural networks with time-varying leakage delays are investigated. By applying Lyapunov functional method and differential inequality techniques, a set of sufficient conditions are obtained for the existence and exponential stability of pseudo almost periodic solutions of the model. Some simulations are carried out to support the theoretical findings. Our results improve and generalize those of the previous studies.

Oscillation of impulsive neutral delay generalized high-order Hopfield neural networks

In this paper, the existence and the exponential stability of piecewise differentiable pseudo-almost periodic solutions for a class of impulsive neutral high-order Hopfield neural networks with mixed time-varying delays and leakage delays are established by employing the fixed point theorem, Lyapunov functional method and differential inequality. Numerical example with graphical illustration is given to illuminate our main results.

Piecewise Pseudo Almost Periodic Solution for Impulsive Generalised High-Order Hopfield Neural Networks with Leakage Delays

Existence of piecewise differentiable pseudo almost-periodic solutions for a class of impulsive high-order Hopfield neural networks with leakage delays are established by employing the fixed point theorem, differential inequality and Lyapunov functionals. The results of this paper are new and they supplement previously known works. Numerical example with graphical illustration is given to illuminate our main results.

Pseudo almost periodic solutions for neutral type high-order Hopfield neural networks with mixed time-varying delays and leakage delays on time scales

In this paper, a class of neutral type high-order Hopfield neural networks with mixed time-varying delays and leakage delays on time scales is proposed. Based on the exponential dichotomy of linear dynamic equations on time scales, Banach's fixed point theorem and the theory of calculus on time scales, some sufficient conditions are obtained for the existence and global exponential stability of pseudo almost periodic solutions for this class of neural networks. Our results are completely new. Finally, we present an example to illustrate our results are effective. Our example also shows that the continuous-time neural network and its discrete-time analogue have the same dynamical behaviors for the pseudo almost periodicity.

Existence and Stability of Pseudo Almost Periodic Solution for Neutral Type High-Order Hopfield Neural Networks with Delays in Leakage Terms on Time Scales

Using the exponential dichotomy of linear dynamic equations on time scales, a fixed point theorem and the theory of calculus on time scales, we obtain some sufficient conditions for the existence and global exponential stability of pseudo almost periodic solutions for a class of neutral type high-order Hopfield neural networks with delays in leakage terms on time scales. Our results show that the continuous-time neural network and its discrete-time analogue have the same dynamical behaviors. Finally, we give a numerical example and simulation to illustrate the feasibility of our results. Results of this paper are completely new even if the time scale $$\mathbb {T}=\mathbb {R}$$T=R or $$\mathbb {Z}$$Z.

Piecewise pseudo-almost periodic solution for impulsive non-autonomous high-order Hopfield neural networks with variable delays

In this paper, we provide a new method to study non-autonomous dynamic systems with variable pseudo-almost periodic coefficients. By using some fixed point theorems in Banach space and inequality technique, some completely new sufficient conditions of the existence and exponential stability of piecewise pseudo-almost periodic solutions are established for impulsive non-autonomous high-order Hopfield neural networks with variable coefficients and delays. Finally, a numerical example and simulations are given to illustrate that the obtained results are feasible and effective. It is the first time that the existence and stability of the pseudo-almost periodic solutions for impulsive nonautonomous neural networks are obtained.

Almost automorphic solution for neutral type high-order Hopfield neural networks with delays in leakage terms on time scales

In this paper, using the existence of the exponential dichotomy of linear dynamic equations on time scales, a fixed point theorem and the theory of calculus on time scales, some sufficient conditions for the existence and global exponential stability of almost automorphic solutions for a class of neutral type high-order Hopfield neural networks (HHNNs) with delays in leakage terms on time scales are obtained. Finally, a numerical example is provided to illustrate the feasibility of our results and our results also show that the continuous-time neural network and its discrete-time analogue have the same dynamical behaviors. The results of this paper are completely new even when the time scale T=R or Z and show that the delays in the leakage term do harm to the existence and global exponential stability of almost automorphic solutions.

Stability and almost periodicity for delayed high-order Hopfield neural networks with discontinuous activations

In this paper, the stability and almost periodicity for delayed high-order Hopfield neural networks with discontinuous activations are investigated. Some new criteria ensuring the existence and global exponential stability of almost periodic solution for the considered neural network model are established by employing the differential inclusion theory, differential inequality technique, and Lyapunov functional approach, the results of this paper improve and complement previously known results. Finally, examples with numerical simulations are presented to demonstrate the effectiveness of theoretical results.

Global Exponential Robust Stability of High-Order Hopfield Neural Networks with S-Type Distributed Time Delays

By employing differential inequality technique and Lyapunov functional method, some criteria of global exponential robust stability for the high-order neural networks with S-type distributed time delays are established, which are easy to be verified with a wider adaptive scope.

Global Exponential Stability of Weighted Pseudo-Almost Periodic Solutions of Neutral Type High-Order Hopfield Neural Networks with Distributed Delays

Some sufficient conditions are obtained for the existence, uniqueness, and global exponential stability of weighted pseudo-almost periodic solutions to a class of neutral type high-order Hopfield neural networks with distributed delays by employing fixed point theorem and differential inequality techniques. The results of this paper are new and they complement previously known results. Moreover, an example is given to show the effectiveness of the proposed method and results.

Global Exponential Stability of Antiperiodic Solution for Impulsive High-Order Hopfield Neural Networks

This paper is concerned with antiperiodic solutions for impulsive high-order Hopfield neural networks with leakage delays and continuously distributed delays. By employing a novel proof, some sufficient criteria are established to ensure the existence and global exponential stability of the antiperiodic solution, which are new and complement of previously known results. Moreover, an example and numerical simulations are given to support the theoretical result.

Anti-periodic solution for impulsive high-order Hopfield neural networks with time-varying delays in the leakage terms

This paper presents new results on anti-periodic solutions for impulsive high-order Hopfield neural networks with time-varying delays in the leakage terms. By employing a novel proof, some criteria are derived for guaranteeing the existence and exponential stability of the anti-periodic solution, which are new and complement previously known results. Moreover, a numerical simulation is given to show the effectiveness.