Existence of almost periodic solution for SICNN with a neutral delay

In this paper, a kind of shunting inhibitory cellular neural network with a neutral delay was considered. By using the Banach fixed point theorem, we established a result about the existence and uniqueness of the almost periodic solution for the shunting inhibitory cellular neural network

On leaderless consensus of fractional-order nonlinear multi-agent systems via event-triggered control

The consensus problem of fractional-order multi-agent systems is investigated by eventtriggered control in this paper. Based on the graph theory and the Lyapunov functional approach, the conditions for guaranteeing the consensus are derived. Then, according to some basic theories of fractional-order differential equation and some properties of Mittag–Leffler function, the Zeno behavior could be excluded. Finally, a simulation example is given to check the effectiveness of the theoretical result

Impulsive mean square exponential synchronization of stochastic dynamical networks with hybrid time-varying delays

This paper investigates the mean square exponential synchronization problem for complex dynamical networks with stochastic disturbances and hybrid time-varying delays, both internal delay and coupling delay are considered in the model. At the same time, the coupled time-delay is also probabilistic in two time interval. Impulsive control method is applied to force all nodes synchronize to a chaotic orbit, and impulsive input delay is also taken into account. Based on the theory of stochastic differential equation, an impulsive differential inequality and some analysis techniques, several simple and useful criteria are derived to ensure mean square exponential synchronization of the stochastic dynamical networks. Furthermore, pinning impulsive strategy is studied. An effective method is introduced to select the controlled nodes at each impulsive constants. Numerical simulations are exploited to demonstrate the effectiveness of the theory results in this paper

Distributed Adaptive Control for Nonlinear Heterogeneous Multi-agent Systems with Different Dimensions and Time Delay

A distributed neural network adaptive feedback control system is designed for a class of nonlinear multi-agent systems with time delay and nonidentical dimensions. In contrast to previous works on nonlinear heterogeneous multi-agent with the same dimension, particular features are proposed for each agent with different dimensions, and similar parameters are defined, which will be combined parameters of the controller. Second, a novel distributed control based on similarity parameters is proposed using linear matrix inequality (LMI) and Lyapunov stability theory, establishing that all signals in a closed loop system are eventually ultimately bounded. The consistency tracking error steadily decreases to a field with a small number of zeros. Finally, simulated examples with different time delays are utilized to test the effectiveness of the proposed control technique

Distributed Adaptive Control for a Class of Heterogeneous Nonlinear Multi-Agent Systems with Nonidentical Dimensions

A novel feedback distributed adaptive control strategy based on radial basis neural network (RBFNN) is proposed for the consensus control of a class of leaderless heterogeneous nonlinear multi-agent systems with the same and different dimensions. The distributed control, which consists of a sequence of comparable matrices or vectors, can make that all the states of each agent to attain consensus dynamic behaviors are defined with similar parameters of each agent with nonidentical dimensions. The coupling weight adaptation laws and the feedback management of neural network weights ensure that all signals in the closed-loop system are uniformly ultimately bounded. Finally, two simulation examples are carried out to validate the effectiveness of the suggested control design strategy

A UNIFIED RISK-BENEFIT ANALYSIS FRAMEWORK FOR INVESTIGATING MOBILE PAYMENT ADOPTION

The paper proposes a unified risk-benefit analysis framework for investigating consumers’ adoption of mobile payment technology. Based on perceived risk theory and risk-benefit analysis literature, the proposed framework integrates three variables—perceived risk, perceived benefit and perceived value, to predict consumers’ intention to use mobile payment. All the proposed hypotheses are well supported based on an empirical validation of 336 useful survey samples. The results show that consumers consider both the beneficial and risky aspects of using mobile payment to evaluate the overall desirability (perceived value) of adoption decision. Further, perceived value, together with perceived risk and benefit directly affects consumers’ intention to adopt the technology. Financial risk is found to be the key resource of the risks of using mobile payment. Both theoretical and practical implications are discussed

Exponential state estimation for competitive neural network via stochastic sampled-data control with packet losses

This paper investigates the exponential state estimation problem for competitive neural networks via stochastic sampled-data control with packet losses. Based on this strategy, a switched system model is used to describe packet dropouts for the error system. In addition, transmittal delays between neurons are also considered. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled-data estimator with probabilistic sampling in two sampling periods is proposed. Then the estimator is designed in terms of the solution to a set of linear matrix inequalities (LMIs), which can be solved by using available software. When the missing of control packet occurs, some sufficient conditions are obtained to guarantee that the exponentially stable of the error system by means of constructing an appropriate Lyapunov function and using the average dwell-time technique. Finally, a numerical example is given to show the effectiveness of the proposed method

Effect of interfacial strain on spin injection and spin polarization of Co2CrAl/NaNbO3/Co2CrAl magnetic tunneling junction

First-principles calculations were carried out to investigate interfacial strain effects on spin injection and spin polarization of a magnetic tunnel junction consisting of half-metallic full-Heusler alloy Co2CrAl and ferroelectric perovskite NaNbO3. Spin-dependent coherent tunneling was calculated within the framework of non-equilibrium Green's function technique. Both spin polarization and tunnel magnetoresistance (TMR) are affected by the interfacial strain but their responses to compressive and tensile strains are different. Spin polarization across the interface is fully preserved under a compressive strain due to stronger coupling between interfacial atoms, whereas a tensile strain significantly enhances interface states and lead to substantial drops in spin polarization and TMR