Patterns governed by chemotaxis and time delay.

In this paper, sufficient conditions of Turing instability are established for general delayed reaction-diffusion-chemotaxis models with no-flux boundary conditions. In particular, we address the difficulty brought about by the time delay in investigating the Turing instability. These models involve the time delay parameter τ and the general density (concentration) function in chemotaxis terms with the chemotaxis parameter χ.

Hopf bifurcation in delayed nutrient-microorganism model with network structure.

In this paper, we introduce and deal with the delayed nutrient-microorganism model with a random network structure. By employing time delay as the main critical value of the Hopf bifurcation, we investigate the direction of the Hopf bifurcation of such a random network nutrient-microorganism model. Noticing that the results of the direction of the Hopf bifurcation in a random network model are rare, we thus try to use the method of multiple time scales (MTS) to derive amplitude equation and determine the direction of the Hopf bifurcation.

Delay-dependent criterion for asymptotic stability of a class of fractional-order memristive neural networks with time-varying delays.

The Lyapunov-Krasovskii functional approach is an important and effective delay-dependent stability analysis method for integer order system. However, it cannot be applied directly to fractional-order (FO) systems. To obtain delay-dependent stability and stabilization conditions of FO delayed systems remains a challenging task.

Turing-Hopf bifurcation analysis in a superdiffusive predator-prey model.

The predator-prey model with superdiffusion is investigated in this paper. Here, the existence of Turing-Hopf bifurcation and the resulting dynamics are studied. To understand such a degenerate bifurcation in the anomalously diffusive system, the weakly nonlinear analysis is employed and the amplitude equations at the Turing-Hopf bifurcation point are obtained.

Patterns induced by super cross-diffusion in a predator-prey system with Michaelis-Menten type harvesting.

Turing instability and pattern formation in a super cross-diffusion predator-prey system with Michaelis-Menten type predator harvesting are investigated. Stability of equilibrium points is first explored with or without super cross-diffusion. It is found that cross-diffusion could induce instability of equilibria.

Stability and synchronization of fractional-order memristive neural networks with multiple delays.

The paper presents theoretical results on the global asymptotic stability and synchronization of a class of fractional-order memristor-based neural networks (FMNN) with multiple delays. First, the asymptotic stability of fractional-order (FO) linear systems with single or multiple delays is discussed. Delay-independent stability criteria for the two types of systems are established by using the maximum modulus principle and the spectral radii of matrices.

Design and implementation of grid multi-scroll fractional-order chaotic attractors.

This paper proposes a novel approach for generating multi-scroll chaotic attractors in multi-directions for fractional-order (FO) systems. The stair nonlinear function series and the saturated nonlinear function are combined to extend equilibrium points with index 2 in a new FO linear system. With the help of stability theory of FO systems, stability of its equilibrium points is analyzed, and the chaotic behaviors are validated through phase portraits, Lyapunov exponents, and Poincaré section.

Sliced Inverse Regression With Adaptive Spectral Sparsity for Dimension Reduction.

Dimension reduction is an important topic in pattern analysis and machine learning, and it has wide applications in feature representation and pattern classification. In the past two decades, sliced inverse regression (SIR) has attracted much research efforts due to its effectiveness and efficacy in dimension reduction. However, two drawbacks limit further applications of SIR.

Stability and synchronization of memristor-based fractional-order delayed neural networks.

Global asymptotic stability and synchronization of a class of fractional-order memristor-based delayed neural networks are investigated. For such problems in integer-order systems, Lyapunov-Krasovskii functional is usually constructed, whereas similar method has not been well developed for fractional-order nonlinear delayed systems. By employing a comparison theorem for a class of fractional-order linear systems with time delay, sufficient condition for global asymptotic stability of fractional memristor-based delayed neural networks is derived.

Linear matrix inequality criteria for robust synchronization of uncertain fractional-order chaotic systems.

This paper is devoted to synchronization of uncertain fractional-order chaotic systems with fractional-order α: 0 < α < 1 and 1 ≤ α < 2, respectively. On the basis of the stability theory of fractional-order differential system and the observer-based robust control, two sufficient and necessary conditions for synchronizing uncertain fractional-order chaotic systems with parameter perturbations are presented in terms of linear matrix inequality, which is an efficient method and could be easily solved by the toolbox of MATLAB. Finally, fractional-order uncertain chaotic Lü system with fractional-order α = 0.