Novel stability criteria for delayed cellular neural networks

In this paper, a new sufficient condition is given for the global asymptotic stability and global exponential output stability of a unique equilibrium points of delayed cellular neural networks (DCNNs) by using Lyapunov method. This condition imposes constraints on the feedback matrices and delayed feedback matrices of DCNNs and is independent of the delay. The obtained results extend and improve upon those in the earlier literature, and this condition is also less restrictive than those given in the earlier references. Two examples compared with the previous results in the literatures are presented and a simulation result is also given.     

Test for increasing convex order in multivariate response

Trend test based on cross-classified data in dose-response has been a central problem in medicine. Most of existing test methods are known to only fit to binary response variables. However, the approaches for binary response tables may suffer from the lack of a clear choice for dichotomization. For multivariate response with ordered categories, some studies have been done for simple stochastic order, likelihood ratio order and so on. However, methods of statistical inference on increasing convex order for more than two multinomial populations have not been fully developed. For testing the increasing convex order alternative, this article provides a model-free test method which can be used in the case of two-way tables and stratified data. Two real examples will be used to illustrate how to apply our test method.     

Synchronization of generalized reaction-diffusion neural networks with time-varying delays based on general integral inequalities and sampled-data control approach

This paper explores the problem of synchronization of a class of generalized reaction-diffusion neural networks with mixed time-varying delays. The mixed time-varying delays under consideration comprise of both discrete and distributed delays. Due to the development and merits of digital controllers, sampled-data control is a natural choice to establish synchronization in continuous-time systems. Using a newly introduced integral inequality, less conservative synchronization criteria that assure the global asymptotic synchronization of the considered generalized reaction-diffusion neural network and mixed delays are established in terms of linear matrix inequalities (LMIs). The obtained easy-to-test LMI-based synchronization criteria depends on the delay bounds in addition to the reaction-diffusion terms, which is more practicable. Upon solving these LMIs by using Matlab LMI control toolbox, a desired sampled-data controller gain can be acuqired without any difficulty. Finally, numerical examples are exploited to express the validity of the derived LMI-based synchronization criteria.     

Positive Periodic Solution for a Neutral Delay Model in Population

IIntroductionIn1993,KuangY［11gavetheopenp毗fern9．2：恤tainsufficientCOnditionsfortheeds－tenceofpO8ltlvependIc劝utionsinN”（t）＝N（t）［a（t）一B（t）N（）一b（t）N（t—r（t））－c（t）N”（t一巾》】（1）wherea（t）,尸（t）,b（t）,c（t）,,（t）are。＂negativecontinuouspo。odlcfunctionswithpenodT．Inthispaper,weconsideraspecialcasewheng（t）。0,b（t）＞0,c（t）。c,r（t）。,,i．e．O鹏idertheexlstenceOfp用tlvependicdutionsOftkforirlBynsf叩ofsomenewtechnicsandtoPOloglcaldeg…     

Finite-time and fixed-time synchronization analysis of inertial memristive neural networks with time-varying delays

This paper investigates the finite-time synchronization and fixed-time synchronization problems of inertial memristive neural networks with time-varying delays. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, several sufficient conditions are derived to ensure finite-time synchronization of inertial memristive neural networks. Then, for the purpose of making the setting time independent of initial condition, we consider the fixed-time synchronization. A novel criterion guaranteeing the fixed-time synchronization of inertial memristive neural networks is derived. Finally, three examples are provided to demonstrate the effectiveness of our main results.     

一类具延时的Hopfield型神经网络模型的周期解

本文利用不动点方法,讨论了一类具延时的Hopfield型神经网络模型周期解的存在性与唯一性,获得了若干充分条件．     

一类具有时滞的神经网络模型的收敛性

运用Lyapunov泛函方法,讨论一类具有时滞的神经网络模型平衡点的全局渐近稳定性,并获得一个新的充分条件．     

Stability analysis of impulsive Cohen-Grossberg neural network with unbounded discrete time-varying delays

In this paper, the impulsive Cohen-Grossberg neural network with unbounded discrete time-varying delays is considered. By using the analysis method and inequality technique, several sufficient conditions are obtained to ensure the global exponential stability of the addressed neural network. These results generalize the existing relevant stability results. Two examples with simulations are given to show the effectiveness of the obtained results.     

The Accumulation of Glycine Betaine Is Dependent on Choline Monooxygenase (OsCMO), Not on Phosphoethanolamine N-Methyltransferase (OsPEAMT1), in Rice (Oryza sativa L. ssp. japonica)

Glycine betaine (GB) is an important osmoprotectant, which improves plant tolerance to various abiotic stresses. In higher plants, GB is synthesized through two-step oxidations of choline, catalyzed by choline monooxygenase (CMO) and betaine aldehyde dehydrogenase (BADH), respectively. Choline, the precursor of GB, is synthesized by phosphoethanolamine N-methyltransferase (PEAMT). Rice is known as a typical non-GB-accumulated species. However, the underlying mechanism related to GB accumulation remains elusive. Here, we determined whether the endogenous accumulation of choline is sufficient to GB biosynthesis in rice and whether the rice CMO protein has the function of oxidizing choline to generate betaine aldehyde. The results showed that overexpression of the rice PEAMT1 gene (OsPEAMT1) resulted in increased levels of choline, while GB content remained unchanged in the transgenic rice plants overexpressing OsPEAMT1. However, the intracellular GB level and the tolerance to salt stress of the transgenic lines overexpressing OsCMO were significantly enhanced. Immunoblotting analysis demonstrated that abundant functional OsCMO proteins with correct size were detected in OsCMO-overexpressing transgenic rice plants, but rarely accumulated in the wild type. Collectively, these results implicated that the endogenous accumulation level of choline is not the major factor leading to non-GB accumulation in rice. Instead, the defective expression of OsCMO resulted in non-GB accumulation.     

A delayed neural network method for solving convex optimization problems

In this paper, the delayed projection neural network for a class of solving convex programming problem is proposed. The existence of solution and global exponential stability of the proposed network are proved, which can guarantee to converge at an exact optimal solution of the convex programming problems. Several examples are given to show the effectiveness of the proposed network.