基于BAM网络的航空雷达在线入侵诊断方法研究

针对传统的航空雷达网络面临的入侵威胁,以及雷达网络存在的入侵诊断检测效率较低,数据匹配速度较慢等问题,提出了一种基于BAM网络的航空雷达在线入侵诊断方法,构建航空雷达在线入侵诊断模型,对航空雷达网络中的外部数据进行预处理,并获取数据特征以及数据特征的可辨识属性矩阵和决策辨识函数,计算测试参数集的所有特征向量,从而使入侵检测算子的匹配量减少,以此提升数据匹配效率,实现对外部入侵数据的过滤检测,从而对雷达数据网络进行在线监控,有效抵御外部异常数据的入侵,保证了航空雷达网络的安全性。仿真结果表明本文方法有效提高了航空雷达网络的在线数据检测匹配速度,诊断准确率达到93.3%,且对航空雷达的入侵诊断检测效率、误报率、漏报率等方面都有明显改善。     

单分子层膜BAM图像的反演

利用电磁场理论,假定单分子层膜凝聚相是单轴晶体,在4×4矩阵方法的基础上,给出了单色平面P偏光以布儒斯特角入射时,经单分子层膜和水相体系反射后的光强表达式。利用检偏角的变化,对布儒斯特显微镜(BAM)图像像素点所代表分子簇的极化倾角和取向方位角进行了参数估计。结果表明:当目标函数取阈值10-40时,此方法的正确概率为0.972 4;取消目标函数阈值的正确概率可提高到0.977 1;若同时增大迭代步数可使正确估计概率提高到0.992 7;增加至6个检偏角,则可给出任意一组极化倾角和取向方位角的正确值。该算法为从BAM图像获取Lang-muir单分子膜的微观分子取向信息提供了一种可能的途径。     

Robust passivity analysis of fuzzy Cohen-Grossberg BAM neural networks with time-varying delays

This paper is concerned with the problem of passivity analysis for a class of Cohen-Grossberg fuzzy bidirectional associative memory (BAM) neural networks with time varying delay. By employing the delay fractioning technique and linear matrix inequality optimization approach, delay dependent passivity criteria are established that guarantees the passivity of fuzzy Cohen-Grossberg BAM neural networks with uncertainties. The passivity condition is expressed in terms of LMIs, which can be easily solved by various convex optimization algorithms. Finally, a numerical example is given to illustrate the effectiveness of the proposed result.     

变时滞中立型BAM神经网络的全局稳定性

本文通过构造新型的Lyapunov-Krasosvskii函数并利用矩阵不等式技巧获得了一类变时滞中立型BAM神经网络的全局稳定性判据,该判据便于应用.数值例子表明,与已有结果相比,该判据具有较低的保守性.     

Robust asymptotic stability for BAM neural networks with time-varying delays via LMI approach

Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix inequality（LMI） conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks.These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs.A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results.     

Stabilization of delayed Cohen‐Grossberg BAM neural networks

This paper deals with finite‐time stabilization results of delayed Cohen‐Grossberg BAM neural networks under suitable control schemes. We propose a state‐feedback controller together with an adaptive‐feedback controller to stabilize the system of delayed Cohen‐Grossberg BAM neural networks. Stabilization conditions are derived by using Lyapunov function and some algebraic conditions. We also estimate the upper bound of settling time functional for the stabilization, which depends on the controller schemes and system parameters. Two illustrative examples and numerical simulations are given to validate the success of the derived theoretical results.     

Existence and global exponential stability of almost periodic solutions for BAM neural networks with distributed leakage delays on time scales

In this paper, we deal with a class of BAM neural networks with distributed leakage delays on time scales. Some sufficient conditions which ensure the existence and exponential stability of almost periodic solutions for such class of BAM neural networks are obtained by applying the exponential dichotomy of linear differential equations, Lapunov functional method and contraction mapping principle. An example is given to illustrate the effectiveness of the theoretical predictions. The obtained results in this paper are completely new and complement the previously known publications.     

GLOBAL DYNAMICS OF DELAYED BIDIRECTIONAL ASSOCIATIVE MEMORY （BAM） NEURAL NETWORKS

IntroductionConsiderthebidirectionalassociativememory (BAM )neuralnetworkswithconstanttransmissiondelaysdescribedbyasystemofdelaydifferentialequationsoftheform[1,2 ]:dxi(t)dt =-aixi(t) nj=1bijfj(yj(t-σij) ) Ii,　　i=1 ,2 ,… ,m ,dyj(t)dt =-cjyj(t) mi=1djigi(xi(t-τji) ) Jj,　　j=1 ,2 ,… ,n ,fort >0 .Thesystem ( 1 )consistsoftwosetsofneurons (orunits)arrangedontwolayers,namely ,I_layerandJ_layer.Inthesystem ( 1 ) ,xi( ·)andyj( ·)denotemembranepotentialoftheithneuronsfromtheI_laye…