一种基于神经网络的组合电路测试生成算法

介绍了一种基于神经网络的组合电路测试生成算法。利用Hopfield神经网络模型将组合电路表示成对应的神经网络,通过建立被测电路的约束网络,构造神经网络的能量函数,使组合电路的测试矢量对应神经网络能量函数的最小值点,使得测试生成问题数学化,并使用遗传算法求解能量函数的最小值点得到故障电路的测试矢量。通过在一些标准电路的实验表明,该测试生成算法有效可行。     

异步四元数神经网络模型的能量函数研究

本文证明了异步离散四元数神经网络模型在其动力学演化过程中，网络的能量函数单调递减，网络最终将稳定在一个平衡态上，存贮图像在能量函数空间中对应一能量极小点，为网络的稳定吸引子。     

异步四元数神经网络模型的能量函数研究

本文证明了异步离散四元数神经网络模型在其动力学演化过程中，网络的能量函数单调递减，网络最终将稳定在一个平衡态上，存贮图像在能量函数空间中对应一能量极小点，为网络的稳定吸引子。     

用于光流计算的一种神经网络算法

本文给出了一种利用神经网络计算光流场的新算法。整个计算过程分为三个阶段:神经网络模型参数的估计,轮廓边界垂直速度分量的动态测量以及光流场的计算。通过网络能量函数和运动的约束误差函数的比较对网络参数进行估计。用一个动态算法迭代运行非线性光流场计算方法以使神经网络能量函数达到最小,同时也对垂直速度分量进行动态估计。由模拟试验结果讨论了影响神经网络收敛性能的若干因素。     

基于优化计算的神经网络能量函数分析

Hopfield神经网络是求解组合优化问题的一种有效方法。将所求问题转化为能量函数是神经网络求解组合优化问题的难点。针对此问题给出了能量函数设计的一般方法和步骤,证明了网络稳定的充分必要条件是网络的解有界。讨论了网络的平衡态与能量函数的极小点的关系,进一步完善了能量函数的方法。作为应用,严格证明了Hopfield神经网络的收敛性,所获结果不仅推广了一些已有的结论,而且为该网络的应用提供了一定的理论基础。     

神经网络在少数投影图像重建中的应用

本文提出了将两种神经网络模型进行套用的图像重建算法。算法将图像重建问题转化为神经网络优化计算,利用Hopfield神经网络（HNN）将各种优化准则转化为模型的能量函数,并应用Adaline网络调整各准则在能量函数中所占的比重,从而求取图像重建的最优解。将其应用于少数投影图像重建,体现出重建精度高、收敛快等特点,取得了令人满意的效果。     

基于混沌神经网络的移动通信信道分配方法研究

该文应用混沌神经网络求解信道分配问题,给出了信道分配的能量函数表达式和混沌神经网络模型,研究了判别混沌神经网络混沌特性的Lyapunov指数法,讨论了网络模型参数对网络混沌特性的影响,提出了基于混沌神经网络的信道分配算法.仿真结果表明,混沌神经网络具有复杂的瞬态混沌特性,它比Hopfield网络具有更强的搜索全局最优解的能力,和更快的收敛速度.     

基于双Sigmoid小波混沌神经网络的盲检测算法*1

接要:针对在盲检测环境中暂态混沌神经网络的缺陷,提出了基于双Sigmoid小波混沌神经网络的盲检测算法,构建了新网络的模型和能量函数,并分别在同步和异步更新模式下证明了该网络的稳定性.新网络的设计思想体现在:采用墨西哥帽小波函数和Sigmoid函数的组合作为网络的激励函数,再为每个神经元加一个激活函数构成双Sigmoid.仿真表明:由于小波函数较强的逼近能力以及双Sigmoid快速收敛的特性,提出的算法显著地提高了网络的全局寻优能力和寻优精度.     

连续型神经网络解决信元调度问题的参数分析

本文利用连续型Hopfield神经网络实现信元调度问题,对采用的新的能量函数进行仿真模拟,通过对网络模型的参数特性进行研究分析,寻找最佳的取值范围,为Hopfield神经网络今后的研究和实际应用提供帮助。     

连续型神经网络解决信元调度问题的参数分析

本文利用连续型Hopfield神经网络实现信元调度问题,对采用的新的能量函数进行仿真模拟,通过对网络模型的参数特性进行研究分析,寻找最佳的取值范围,为Hopfield神经网络今后的研究和实际应用提供帮助.     

RESEARCHES ON THE STABILITIES OF ANALOG ELECTRONIC NEURAL NETWORKS

A new method for analyzing the stabilities of analog electronic neural networks ispresented.The energy functions with clear physical meaning are derived by introducing the staticequivalent circuit models,which has expanded the Tellegen Theorem for application on circuitanalysis.The method used to derive the energy functions of nets from first order differentialequations is valid for all first order continuous autonomous systems.The stability analysis ofcellular neural networks is made by the use of the stationary cocontent theorem.Some resultsare instructive for the network implementation on circuits.     

Researches on the stabilities of analog electronic eural networks

A new method for analyzing the stabilities of analog electronic neural networks is presented. The energy functions with clear physical meaning are derived by introducing the static equivalent circuit models, which has expanded the Tellegen Theorem for application on circuit analysis. The method used to derive the energy functions of nets from first order differential equations is valid for all first order continuous autonomous systems. The stability analysis of cellular neural networks is made by the use of the stationary cocontent theorem. Some results are instructive for the network implementation on circuits.     

数字电路的最优神经网络模型及建立方法

本文研究电路的最优神经网络模型,获得了对任意结构的多输入多输出逻辑电路,都存在一种最优神经网络能表征电路的逻辑功能,通过求解一个线性方程组可以得到这种神经网络的结构.文中也给出了多输入基本门电路的最优神经网络结构及其能量函数的一般表达式.     

基于PSO-RBF神经网络的模拟电路故障诊断研究

针对模拟电路故障诊断的难点和传统诊断方法的不足之处,提出了一种基于PSO算法优化的RBF神经网络模拟电路故障诊断方法。为了约简网络结构从而提高诊断效率,采用主成分分析方法对故障特征进行有效提取。针对RBF网络传统训练算法中隐层节点中心及基函数宽度选取困难问题,提出采用PSO算法来优化训练RBF网络,以提高网络的训练速度和泛化性能。最后,通过电路仿真对所提方法的有效性进行了验证。     

System identification and modelling based on a double modified multi-valued neural network

A novel identification technique for the extraction of lumped circuit models of general distributed or stray devices is presented. The approach is based on two multi-valued neuron neural networks used in a joined architecture able to extract hidden parameters. The convergence allows the validation of the approximated lumped model and the extraction of the correct values. The inputs of the neural network are the geometrical parameters of a given structure, while the outputs represent the estimation of the lumped circuit parameters. The method uses a frequency response analysis approach in order to elaborate the data to present to the net.     

基于MIV和BRBP神经网络的电路板红外诊断方法

针对BP神经网络对于海量数据训练及多维数据训练收敛困难的问题,在使用增加动力项、自适应学习速率等方法的基础上,引入均值影响度算法(MIV)构造了贝叶斯正则化反向传播(BRBP)神经网络,以此提高电子线路板红外故障诊断算法的效率。利用红外测温方式,获取了不同室温及运行状态下电路板中21个元器件温度数据。将此21个参数作为故障诊断模型的初始输入变量,经过MIV算法简约为12个参数输入至BRBP神经网络,进行故障评估和诊断。结果表明:相对于传统的BRBP神经网络,本文设计的基于MIV和BRBP神经网络模型诊断方法极大简化了数据训练的数据量并解决了数据收敛的困难,因此效率更高,用时更省。     

Qualitative analysis for recurrent neural networks with linear threshold transfer functions

Multistable networks have attracted much interest in recent years, since multistability is of primary importance for some applications of recurrent neural networks where monostability exhibits some restrictions. This paper focuses on the analysis of dynamical property for a class of additive recurrent neural networks with nonsaturating linear threshold transfer functions. A milder condition is derived to guarantee the boundedness and global attractivity of the networks. Dynamical properties of the equilibria of two-dimensional networks are analyzed theoretically, and the relationships between the equilibria features and network parameters (synaptic weights and external inputs) are revealed. In addition, the sufficient and necessary conditions for coexistence of multiple equilibria are obtained, which confirmed the observations in with a cortex-inspired silicon circuit. The results obtained in this paper are applicable to both symmetric and nonsymmetric networks. Simulation examples are used to illustrate the theory developed in this paper.     

Crosstalk and transient analyses of high-speed interconnects andpackages

A multiconductor interconnect is modeled using resistors and linear-dependent current and voltage sources. The analysis of a high-speed circuit including lossy interconnection buses is then reduced to simulation of the circuit together with the equivalent circuits of the interconnects. The authors present a new method for the crosstalk and transient analysis of lossy interconnects with arbitrary termination circuits. In order to analyze an interconnect containing N signal conductors, they derive closed-form formulas to determine its transfer functions, and they apply the inverse Fourier transform to obtain its time-domain pulse response functions. Two types of equivalent circuit models can be formulated once the pulse response functions of the interconnect are found. The circuit schematics of the models depend on the number of the signal conductors, irrespective of the physical parameters of the interconnect. These models are compatible with standard circuit simulation tools since they consist of linear resistive networks and linear-dependent sources only. Two example circuits are studied to examine the accuracy and efficiency of the method     

A NEW METHOD FOR EIGENSTRUCTURES EXTRACTION AND ITS NEURAL NETWORKS IMPLEMENTATION

The cost function for eigenstructures extraction is discussed in detail in this paper, one can obtain the largest eigenvector by minimizing the cost function. In order to obtain other eigenvectors, a covariance matrix series is constructed. If one compares the cost function with the energy function of a neural networks, the neural networks can be easily introduced to extract the eigenvectors. Theoretical analysis and computer simulations show that the proposed method is reasonable and feasible.     

MPLS网络中的OAM机制

随着许多传统的基于电路交换的应用被放到基于包交换的IP／MPLS网络或者以太网上进行传输，包交换网络和电路交换网络之间的界限逐渐消失。为了提供和电路交换网络相同的QoS保证，包交换网络中也必须具备相同的OAM功能。首先对现有的基于IP的OAM功能做一介绍，然后就基于MPLS的包交换网络中如何提供OAM功能进行分析，最后讨论近期有关MPLS网络中OAM功能的一些建议。