Global Stability of a Discrete Population Dynamics Model with Two Delays

A population dynamics model \(x_n = \frac{{\alpha x_{n - m} }}{{1 + x_{n - m} + \beta x_{n - k} }}\) with two delays k and m and coefficients α > 1 and β ≥ 0 is studied. A sufficient condition, which is also necessary for certain delays, for the global asymptotic stability of the stationary solution \(x_n = \frac{{\alpha - 1}}{{\beta + 1}}\) is formulated.     

速度时滞反馈控制下磁浮系统的稳定性与Hopf分岔

The problem of time delay speed feedback in the control loop is considered here.Its effects on the linear stability and dynamic behavior of the maglev system are investigated.It is found that a Hopf bifurcation can take place when the time delay exceeds certain values.The stability condition of the maglev system with the time delay is acquired.The direction and stability of the Hopf bifurcation are determined by constructing a center manifold and by applying the normal form method.Finally,numerical simulations are performed to verify the analytical result.     

Robust Stability Criterion for Delayed Neural Networks with Discontinuous Activation Functions

The problem of global robust stability for a class of uncertain delayed neural networks with discontinuous activation functions has been discussed. The uncertainty is assumed to be of norm-bounded form. Based on Lyapunov–Krasovskii stability theory as well as Filippov theory, the conditions are expressed in terms of linear matrix inequality, which make them computationally efficient and flexible. An illustrative numerical example is also given to show the applicability and effectiveness of the proposed results.     

On Global Stability of Delayed BAM Stochastic Neural Networks with Markovian Switching

In this paper, the stability analysis problem is investigated for stochastic bi-directional associative memory (BAM) neural networks with Markovian jumping parameters and mixed time delays. Both the global asymptotic stability and global exponential stability are dealt with. The mixed time delays consist of both the discrete delays and the distributed delays. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, we employ the Lyapunov–Krasovskii stability theory and the Itô differential rule to establish sufficient conditions for the delayed BAM networks to be stochastically globally exponentially stable and stochastically globally asymptotically stable, respectively. These conditions are expressed in terms of the feasibility to a set of linear matrix inequalities (LMIs). Therefore, the global stability of the delayed BAM with Markovian jumping parameters can be easily checked by utilizing the numerically efficient Matlab LMI toolbox. A simple example is exploited to show the usefulness of the derived LMI-based stability conditions.     

Stability Analysis of High-order Proportional Delayed Cellular Neural Networks with D Operators

International Journal of Control, Automation and Systems - In this paper, we focus on a class of high-order proportional delayed cellular neural networks involving D operator. First of all, the...     

Stability and Synchronization Analysis of Discrete-Time Delayed Neural Networks with Discontinuous Activations

Neural Processing Letters - In this paper, both stability and synchronization of discrete-time delayed neural networks with discontinuous activations are investigated. By means of functional...     

Receding Horizon Stability Analysis of Delayed Neural Networks with Randomly Occurring Uncertainties

For delayed neural networks with randomly occurring uncertainties (ROU), this paper uses an improved integral inequality to optimize the stability of the receding horizon. The ROU follows some uncorrelated Bernoulli distribution white noise sequence, which it can enter the neural network in a free and random manner. By using a suitable lemma, the ROU problem added in this paper is transformed into a linear matrix inequality. Based on the auxiliary function-based integral inequality method, the new cross terms matrix of linear matrix inequality in the improved Lyapunov-Krasovskii functional is processed. Therefore, some new matrix variables containing more information are generated, so that the results have more degrees of freedom. This paper has obtained the new condition of the end-weighting matrix of the receding horizon cost function, thereby reducing its conservativeness and increasing its upper limit of delay. Finally, the superiority of the method has be illustrated by giving some simulation numerical examples.

     

Exponential Stability of Switched Time-varying Delayed Neural Networks with All Modes Being Unstable

This paper aims to design an appropriate switching law to stabilize the switched neural networks with time-varying delays when all subsystems are unstable. By using the discretized Lyapunov function approach and the extended comparison principle for impulsive systems, the stability of switched delayed neural networks composed full of unstable subsystems is analyzed and a computable sufficient condition is derived in the framework of dwell time. The effectiveness of the proposed results is illustrated by a numerical example.     

基于 M 矩阵理论的时滞细胞神经网络稳定性分析*

研究了时滞细胞神经网络的稳定性问题。通过M‐矩阵理论及其判定引理,运用适当的线性参数变换,推导出时滞细胞神经网络的稳定性条件,相比常用的Lyapunov方法,论文为研究多时滞细胞神经网络的稳定性提供了一个更为简单的新方法,降低了原有结论的保守性,进一步推导完善了全局渐近稳定平衡点为原点时的充分条件。仿真实例证明了文章提供的方法有效可行。     

A Dynamic Model of Unemployment with Migration and Delayed Policy Intervention

The purpose of this paper is to build and analyse a model of unemployment, where jobs search is open to both natives and migrant workers. Markets and government intervention respond jointly to unemployment when creating new jobs. Full employment of resources is the focal point of policy action, stimulating vacancy creation. We acknowledge that policy is implemented with delays, and capture labour market outcomes by building a non-linear dynamic system. We observe jobs separation and matching, and extend our model to an open economy with migration and delayed policy intervention meant to reduce unemployment. We analyse the stability behaviour of the resulting equilibrium for our dynamic system, including models with Dirac and weak kernels. We simulate our model with alternative scenarios, where policy action towards jobs creation considers both migration and unemployment, or just unemployment.     

如何用经验Logistic回归方法估计Rasch模型中参数

将经验Logistic回归方法用于项目反应理论中Rasch模型的参数估计,通过蒙特卡洛模拟表明这种方法对项目参数的估计较好,这种方法是对极大似然估计方法的不足的一种补充。     

城市车辆配送线路设计的模型与算法

文章建立了城市车辆配送的多旅行商问题(MTSP)模型,设计了一个新的遗传算法,用2Opt算子改进解的总距离,用贪心交叉算子提供变异功能,计算实例表明了该算法的有效性。     

Global Robust Exponential Stability for Interval Delayed Neural Networks with Possibly Unbounded Activation Functions

In this paper, we mainly study the global robust exponential stability of the neural networks with possibly unbounded activation functions. Based on the topological degree theory and Lyapunov functional method, we provide some new sufficient conditions for the global robust exponential stability. Under these conditions, we prove existence, uniqueness and global robust exponential stability of equilibrium point. In the end, some examples are provided to demonstrate the validity of the theoretical results.     

SIMULINK仿真模型稳定性的研究

该文以同步电机为例,介绍了根据系统微分方程,应用MATLAB SIMULIK软件包构造两种SIMULINK仿真模型的方法。根据派克坐标变换,将同步电机在abc坐标下的数学模型变换为dq坐标下同步电机数学模型;利用后向欧拉法将dq轴下同步电机数学模型中微分方程进行变换,形成另一种同步电机数学模型。运用SIMULINK软件构建这两种同步电机仿真模型,然后,再应用这两种仿真模型搭建两个简单的电力系统来做仿真试验,并讨论两种仿真模型仿真方法的稳定性。仿真结果显示,该模型与仿真方法是正确的,进而扩展了SIMULINK仿真模型的实用范围。     

Exponential Stability and Sampled-Data Synchronization of Delayed Complex-Valued Memristive Neural Networks

Neural Processing Letters - This paper is devoted to the global exponential stability (GES) and synchronization control of delayed complex-valued memristive neural networks (CVMNNs). The criterion...     

棉花物流运输路径建模及仿真应用研究

分析我国棉花物流运输现状及问题,以降低运输成本为目标,综合考虑发货点、收货点、运输方式、油费、路桥费、运输时间等因素,建立棉花运输路径模型,运用前k条最短路径算法求解最优路径.     

一种使用多Filter初始化GA种群的混合特征选择模型

特征选择已经是高维数据处理尤其是模式识别领域中的一个关键问题.提出一种混合特征选择模型用于从潜在的相关特征中选择那些最重要的特征.该模型包括两部分:filter部分与wrapper部分.在filter部分,4种不同的Filter方法分别对候选特征进行独立排序,在融合后进一步生成综合特征排序,综合排序随后产生遗传算法(GA)的初始种群.在wrapper部分,GA算法根据神经网络的分类准确率对个体(特征子集)进行评价,以便于搜索到最优的特征子集.测试结果表明,该模型不仅能有效地减少特征子集的大小,而且还可以进一步提高分类识别的准确率和效果.