Stability analysis of BAM neural networks with time-varying delays

Some new criteria for the global asymptotic stability of the equilibrium point for the bi-directional associative memory neural networks with time varying delays are presented. The obtained results present the structure of linear matrix inequality which can be solved efficiently. The comparison with some previously reported results in the literature demonstrates that the results in this paper provide one more set of criteria for determining the stability of the bi-directional associative memory neural networks with delays.     

ABOUT HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS

In this paper, hybrid bidirectional associative memory neural networks with discrete delays is considered. By ingeniously importing real parameters di > 0(i = 1,2,···,n) which can be adjusted, we establish some new sufficient conditions for the dynamical characteristics of hybrid bidirectional associative memory neural networks with discrete delays by the method of variation of parameters and some analysis techniques. Our results generalize and improve the related results in [10,11]. Our work is significant...     

Luminescence efficiencies of self- and Tm activated CaWO4 under vacuum ultraviolet radiation excitation

Luminescence efficiency of self-activated CaWO₄ under 147 nm vacuum ultraviolet (VUV) radiation excitation is about 90% of that of BaMgAl₁₀O₁₇:Eu²⁺ (BAM), the commercial blue plasma display panel (PDP) phosphor. However, the color purity and the particle size of the former needs substantial modification before it can be considered for application in PDP. CaWO₄:Tm exhibits Tm³⁺ emission peaks in the blue region due to energy transfer from WO₄ to Tm³⁺ ions but the overall emission intensity under 147 nm excitation is reduced when compared to that of CaWO₄.     

GLOBAL EXPONENTIAL STABILITY IN HOPFIELD AND BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH TIME DELAYS

Without assuming the boundedness, strict monotonicity and differentiability of the activation functions, the authors utilize the Lyapunov functional method to analyze the global convergence of some delayed models. For the Hopfield neural network with time delays, a new sufficient condition ensuring the existence, uniqueness and global exponential stability of the equilibrium point is derived. This criterion concerning the signs of entries in the connection matrix imposes constraints on the feedback matrix independently of the delay parameters. From a new viewpoint, the bidirectional associative memory neural network with time delays is investigated and a new global exponential stability result is given.     

带分布时滞的双向联想记忆神经网络周期解的存在性

讨论了-类带分布时滞的双向联想记忆神经网络周期解的存在性.通过运用Mawhin迭合度连续性定理,和-些不等式的分析技巧,得到了-些保证周期解存在的充分条件.     

多重训练BAM算法中强化系数的解析求取

从ＢＡＭ的能量函数出发推导出强化系数的解析解形式。在强化系数的作用下，ＢＡＭ网将被强化矢量对存储在皮知矢量对为中心Ｈｅｍｍｉｎｇ距离为１的邻域里的能量最小点，人而保证了该矢量对的正确联想。给出了根据此解析算式编制的算法和计算实例。     

Darobactin B Stabilises a Lateral-Closed Conformation of the BAM Complex in E. coli Cells

The β-barrel assembly machinery (BAM complex) is essential for outer membrane protein (OMP) folding in Gram-negative bacteria, and represents a promising antimicrobial target. Several conformational states of BAM have been reported, but all have been obtained under conditions which lack the unique features and complexity of the outer membrane (OM). Here, we use Pulsed Electron-Electron Double Resonance (PELDOR, or DEER) spectroscopy distance measurements to interrogate the conformational ensemble of the BAM complex in E. coli cells. We show that BAM adopts a broad ensemble of conformations in the OM, while in the presence of the antibiotic darobactin B (DAR-B), BAM′s conformational equilibrium shifts to a restricted ensemble consistent with the lateral closed state. Our in-cell PELDOR findings are supported by new cryoEM structures of BAM in the presence and absence of DAR-B. This work demonstrates the utility of PELDOR to map conformational changes in BAM within its native cellular environment.     

BAM神经网络的稳定性分析

本文研究了BAM神经网络的稳定性问题,通过构造新的Lyapunov泛函,给出了此类系统平衡点渐近稳定的充分条件,并举例说明了结果的有效性和保守性.该充分条件对丰富和完善了神经网络的稳定性理论体系,具有积极的意义.     

具有变系数和变时滞的BAM神经网络的全局指数稳定性的新判据

研究了一类具有变系数和变时滞的联想记忆神经网络的全局指数稳定性。通过选择适当的Lyapunov-krasovskii泛函,利用不等式技巧给出了联想记忆神经网络的全局指数稳定性的新判据。     

分布时滞模糊BAM神经网络模型的指数稳定性

详细介绍了一类具分布时滞的模糊BAM（Bi·direction Associate Memory）神经网络模型．在合适的条件下,通过数学分析技巧和压缩映像原理,证明了模型平衡点的存在唯一性;通过构造Liapunov泛函,利用Lipschitz激励函数性质,获得了平衡点的指数稳定性．