Existence and global stability of periodic solution for impulsive predator-prey model with diffusion and distributed delay

In this paper, the nonlinear nonautonomous predator-prey model with impulsive effect is considered. By using the method of coincidence degree theorem, a set of easily verifiable sufficient conditions are obtained for the existence of at least one strictly positive periodic solution, and by the means of a suitable Lyapunov function, the uniqueness and global attractivity of positive periodic solution are presented. An example shows the feasibility of our main results.     

Global exponential stability of non-autonomous FCNNs with Dirichlet boundary conditions and reaction–diffusion terms

In this paper, we investigate the global exponential stability of non-autonomous fuzzy cellular neural networks (FCNNs) with Dirichlet boundary conditions and reaction–diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain some sufficient conditions for the uniqueness and global exponential stability of the equilibrium solution. The result is easy to check and plays an important role in the design and applications of globally exponentially stable fuzzy neural circuits. Finally, the utility of our result is illustrated via a numerical example.     

Existence and global attractivity of positive periodic solutions for impulsive predator–prey model with dispersion and time delays

In this paper, we study the existence and global attractivity of positive periodic solutions for impulsive predator–prey systems with dispersion and time delays. By using the method of coincidence degree theorem, a set of easily verifiable sufficient conditions are obtained for the existence of at least one strictly positive periodic solution, and by means of a suitable Lyapunov functional, the uniqueness and global attractivity of the positive periodic solution are presented. Some known results subject to the underlying systems without impulses are improved and generalized.     

New sufficient conditions for global asymptotic stability of Cohen–Grossberg neural networks with time-varying delays

In this paper, a class of Cohen–Grossberg neural networks with time-varying delays are considered. Without assuming the boundedness and monotonicity of activation functions, we establish new sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point for such delayed Cohen–Grossberg neural networks. Numerical examples are provided to show that the proposed criteria are less conservative than some results in the literature.     

Delay-dependent exponential stability for impulsive Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion terms

In this paper, a class of impulsive Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion is formulated and investigated. By employing delay differential inequality and the linear matrix inequality (LMI) optimization approach, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive Cohen–Grossberg neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of Cohen–Grossberg neural networks. An example is given to show the effectiveness of the results obtained here.     

Mean square exponential stability of stochastic neural networks with reaction–diffusion terms and delays

In this paper, some sufficient conditions ensuring mean square exponential stability of the equilibrium point of a class of stochastic neural networks with reaction–diffusion terms and time-varying delays are obtained. The conditions involving the effect of diffusion terms reduce the conservatism of the previous results. Finally, we give a numerical example to verify the effectiveness of our results.     

Stochastic exponential robust stability of interval neural networks with reaction–diffusion terms and mixed delays

The stochastic exponential robust stability is considered for a class of delayed neural networks with reaction–diffusion terms and Markov jumping parameters in this paper. It is assumed that the uncertain weight matrices belong to the given interval matrices. Some sufficient conditions for the stochastic exponential robust stability of the system are established by applying vector Lyapunov function method and M-matrix theory. The obtained results involving the effect of reaction–diffusion improve the existing conditions. Finally, two examples with numerical simulations are given to illustrate the obtained results.     

Almost periodic solutions for Lotka–Volterra systems with delays

This paper studies a general class of delayed almost periodic Lotka–Volterra system with time-varying delays and distributed delays. By using the definition of almost periodic function, the sufficient conditions for the existence and uniqueness of globally exponentially stable almost periodic solution are obtained. The conditions can be easily reduced to special cases of cooperative systems and competitive systems.     

Global stability of a class of Nicholson’s blowflies model with patch structure and multiple time-varying delays

This paper is concerned with a class of the generalized Nicholson’s blowflies model with patch structure and multiple time-varying delays, which is defined on the nonnegative function space. Under proper conditions, we employ a novel proof to establish some criteria of the global stability of positive equilibrium for this model. Moreover, we give an example to illustrate our main result.     

Almost periodic solutions of a discrete Lotka–Volterra competition system with delays

In this paper, we consider a discrete almost periodic Lotka–Volterra competition system with delays. Sufficient conditions are obtained for the permanence and global attractivity of the system. Further, by means of an almost periodic functional hull theory, we show that the almost periodic system has a unique strictly positive almost periodic solution, which is globally attractive. Some examples are presented to verify our main results.     

Global robust stability criteria of stochastic Cohen–Grossberg neural networks with discrete and distributed time-varying delays

The paper is concerned with the problem of robust asymptotic stability analysis of stochastic Cohen–Grossberg neural networks with discrete and distributed time-varying delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technology, some sufficient conditions are derived to ensure the global robust convergence of the equilibrium point. The proposed conditions can be checked easily by LMI Control Toolbox in Matlab. Furthermore, all the results are obtained under mild conditions, assuming neither differentiability nor strict monotonicity for activation function. A numerical example is given to demonstrate the effectiveness of our results.     

Global exponential stability and periodicity of reaction–diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions

In this paper, global exponential stability and periodicity of a class of reaction–diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions are studied by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential convergence to 0 of the difference between any two solutions of the original neural networks, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Secondly, we prove periodicity. Sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the equilibrium and periodic solution are given. These conditions are easy to verify and our results play an important role in the design and application of globally exponentially stable neural circuits and periodic oscillatory neural circuits.     

Oscillation and global asymptotic stability of a discrete haematopoiesis model

In this paper, we study a discrete haematopoiesis model $$\Delta y_{n}=-\alpha y_{n}+\frac{\beta y_{n-k}^{m}}{1+y_{n-k}^{p}}\quad\mbox{for}\ n=0,1,\ldots,$$ where α∈(0,1), β∈(α,∞), m∈(1,∞), p∈[m,∞), k∈?, and establish some results about oscillation and global asymptotic stability of the positive solution of this model.     

Existence and multiplicity of periodic solutions for the ordinary p-Laplacian systems

Some existence and multiplicity results are obtained for periodic solutions of the ordinary p-Laplacian systems: $$\left\{\begin{array}{@{}l@{\quad{}}l}(|u'(t)|^{p-2}u'(t))'=\nabla F(t,u(t)),&\mbox{a.e. }t\in[0,T],\\[4pt]u(0)-u(T)=u'(0)-u'(T)=0\end{array}\right.$$ by using the Saddle Point Theorem, the least action principle and the Three-critical-point Theorem.     

Improved stability criteria for neutral type Lur’e systems with time-varying delays

In this letter, the problem of stability analysis for neutral type Lur’e systems with interval time-varying delays is considered. By introducing a novel Lyapunov–Krasovskii (L–K) functional and utilizing second order reciprocal convex combination technique and Finsler lemma, an improved delay-dependent stability criteria is derived in terms of linear matrix inequalities (LMIs). The information about the time-varying delays and their upper bounds are fully used in the L–K functional. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.     

Existence of a periodic solution for continuous and discrete periodic second-order equations with variable potentials

Green’s functions for new second-order periodic differential and difference equations with variable potentials are found, then used as kernels in integral operators to guarantee the existence of a positive periodic solution to continuous and discrete second-order periodic boundary value problems with periodic coefficient functions. A new version of the Leggett-Williams fixed point theorem is employed.     

Existence and globally exponential stability of almost periodic solution for Cohen–Grossberg BAM neural networks with variable coefficients

In this paper, a class of Cohen–Grossberg BAM neural networks with variable coefficients are studied. Some sufficient conditions are established for the existence and uniqueness of the almost periodic solution. These results have important leading significance in designs and applications of Cohen–Grossberg BAM neural networks. Finally, an example is presented to illustrate the feasibility and effectiveness of the results.