椭圆球面波函数的快速重构算法

椭圆球面波函数是一种极具应用前景的非正弦函数。针对现有求解算法效率低,不易硬件实现的问题,基于带通采样定理并结合椭圆球面波函数的时限带限特性,提出了一种快速重构算法并推导得出了重构函数,给出了近似解析解表达式,对比分析了算法的求解精度和计算复杂度。结果表明:该算法求解速度快、精度高、计算复杂度低,可用于任意频段上的椭圆球面波函数的数值求解。该算法更适于硬件实现。     

求解电场积分方程的高阶矩量法

高阶矩量法越来越多地用于求解目标散射问题。该文对高阶矩量法中的若干问题进行了分析。由于高阶矩量法需处理多重数值积分,该文提出了一种把多重数值积分转化为矩阵乘积的方法。对于矩量法阻抗矩阵的填充,每次只需计算积分节点上格林函数的值,大大缩短矩阵填充时间,提高了高阶距量法的效能。对奇异(准奇异)积分的不同处理方法做了总结,并对其优劣做了评价。     

用电磁场算子理论求波导复合系统的本征值

该文用并矢格林函数方法通过虚拟边界的电场和磁场的耦合求解波导复合结构的本征值,所采用的并矢格林函数没有奇异项,可以用标量格林函数来表示并进行计算,因此不仅可以计算横电与横磁模的基模和高次模式,还可以计算存在两个孪生模式的复合系统。该文同时还对经典场论中常用的一些定理,如面旋度定理进行了探讨。     

Marcum Q 函数数值计算方法

Marcum Q 函数是通信对抗理论分析中会遇到的一种积分上限为无穷大的广义积分函数,如采用常规的数值积分法,常常会出现计算溢出使算法不稳定。本文提出了一种新的计算方法,算法采用解析积分将原广义积分恒等变换为常义积分,然后再采用常规的数值积分算法完成计算。该算法非常稳定,可完全避免溢出。新算法的稳定性是以增加计算量为代价的,因此,该算法非常适合对计算效率要求不高、函数宗量的取值又比较大的Marcum Q 函数的数值计算。     

基于数值积分卡尔曼-概率假设密度滤波的多说话人跟踪方法

针对多说话人跟踪的非线性系统模型,提出了一种基于数值积分卡尔曼-概率假设密度滤波的多说话人跟踪方法。该方法采用麦克风阵列的时间延迟估计作为观测数据,利用具有三次代数精度的球面-径向数值积分准则计算非线性系统贝叶斯滤波器中的多维积分,通过数值积分卡尔曼滤波和概率假设密度滤波对后验多说话人状态的一阶统计量进行估计,并通过递推更新得到说话人状态信息,实现非线性高斯系统的多说话人跟踪。该方法无需求解非线性系统函数的雅克比矩阵,且计算量较小。仿真实验分析了检测概率、虚警点数目、采样周期、信噪比以及混响时间变化时跟踪算法的性能。实验结果表明,该方法降低了系统模型非线性对滤波算法的影响,增强了跟踪算法的鲁棒性,提高了说话人状态和数目的估计精度。      

神经网络优化计算的新方法

本文在Hopfield神经网络优化方法的基础上，根据模拟退火算法逃离局部最优解的原理，提出了一种神经网络优化计算的新方法．通过调整神经网络的连接权，网络的演化不仅可以逃离目标函数的局部最优解，而且可以改善目标函数的局部最优解．实验结果表明，新方法求解最优解所需的计算时间比模拟退火算法少得多．     

基于半正定松弛的MIMO盲检测

为解决MIMO系统盲检测问题,该文以最大似然序列检测为估计准则,通过推导建立了一种新的半正定松弛(SemiDefinite Relaxation, SDR)求解模型,使得到的松弛解的秩等于发送天线数。为了解决了松弛解秩大于1时估计原始发送序列的难题,该文提出一种特征向量近似法和随机法相结合的方法。通过限定目标函数的取值上限,使算法能够根据目标函数值自适应判断求解发送序列个数,从而减少每次求解的约束个数和SDR的求解次数,分析表明算法的计算复杂度与发送天线数成线性关系。最后,通过仿真表明所提算法能够在与秩1的算法性能保持相当的条件下减少计算时间,并验证了算法计算复杂度与发送天线数成线性关系。     

复数离散Hopfield网络盲检测64QAM信号

针对复数多电平QAM信号的盲检测问题,该文提出了一个新的复数离散多电平Hopfield神经网络。该网络的实部、虚部各含一个多电平离散激励实函数。该文分析了经典两电平离散Hopfield神经网络能量函数的局限性,构造了一个新的复数多电平神经网的能量函数,并用此能量函数讨论了神经网的稳定性。当该神经网的权矩阵借助接收数据补投影算子构成时,该复数离散多电平Hopfield网络可有效地求解带整数约束的二次规划问题,从而实现QAM信号盲检测。仿真试验表明:该算法所需接收数据较短,就可到达全局真平衡点,计算难度大大降低,具有良好的快速性。     

基于稀疏约束神经网络的红外弱小目标检测技术

提出一种基于稀疏约束的神经网络模型和完备的矩特征集,用于提高红外弱小目标检测性能。传统的神经网络分类模型往往采用SoftMax, Logistic回归等激活函数,而本文设计更为简单的符号函数作为分类层激活函数,并通过逐步回归的方式实现对参数的求解。同时,为了降低计算复杂度、提高算法性能,目标函数加入了范数约束使得参数具有一致性并使其稀疏化。实验结果显示,所提算法相比传统的方法性能更好,且能够达到实时处理要求。     

二维线性相位FIR数字滤波器的优化设计

该文提出了一种用神经网络算法来设计二维线性相位数字滤波器的新方法。通过分析二维FIR线性相位滤波器的幅频响应特性,建立了神经网络算法。根据给定的幅频响应指标,按该算法可获得滤波器系数。为保证该算法的稳定性,提出并证明了该算法的收敛定理。文中给出了圆对称和矩形对称二维低通线性相位FIR数字滤波器优化设计实例。计算机仿真结果表明由该方法设计的二维数字滤波器,通带和阻带范围波动小,所需计算量非常少,稳定性强,因而是一种优异的设计方法。     

Hybrid identification of nuclear power plant transients with artificial neural networks

Proper and rapid identification of malfunctions (transients) is of premier importance for the safe operation of nuclear power plants. Feedforward neural networks trained with the backpropagation (BP) algorithm are frequently applied to model simulated nuclear power plant malfunctions. The correct identification of unlabeled transients-or transients of the "don't-know" type have proven to be especially challenging. A novel hybrid neural network methodology is presented which also correctly classifies the unlabeled transients. From this analysis the importance for properly accommodating practical aspects such as the drift of electronics elements of a simulator, the digitization of simulated and actual plant signals, and the accumulating errors during numerical integration became obvious. Beside the feedforward neural networks trained with the BP algorithm, many other types of networks and codes were used for finding the best (sensitive and robust) algorithms. Various neural network based models were successfully applied to identify labeled and unlabeled malfunctions of the Hungarian Paks nuclear power plant simulator. The BP and probabilistic methods have been proven as the most robust against the misleading recognition of unlabeled malfunctions.     

Numerical accuracy of multipole expansion for 2D MLFMA

A numerical study of the multipole expansion for the multilevel fast multipole algorithm (MLFMA) is presented. In the numerical implementation of MLFMA, the error comes from three sources: the truncation of the addition theorem; the approximation of the integration; the aggregation and disaggregation process. These errors are due to the factorization of the Green's function which is the mathematical core of the algorithm. Among the three error sources, we focus on the truncation error and a new approach of selecting truncation numbers for the addition theorem is proposed. Using this approach, the error prediction and control can be improved for the small buffer sizes and high accuracy requirements.     

平坦瑞利环境下多用户检测的量子神经网络方法

提出采用量子神经网络(QNN)方法在平坦瑞利环境下进行多用户检测的方法。量子神经网络是量子计算与人工神经网络(ANN)相结合的产物,由于利用量子并行计算和量子纠缠等特性从而克服了传统人工神经网络的固有缺点。研究结果表明:该算法具有较强的鲁棒性;能有效地抑制噪声干扰,克服远近效应,在平坦瑞利衰减下具有较好地误码性能。     

非对称模板点格神经网络的动态特性研究

本文利用了矩阵Ｏｓｔｒｏｗｓｋｉ与ｂＲＡＵＥＲ对角占优之条件得到了点格神经网络平衡点存在的充分必要条件，然后利用Ｏｓｔｒｏｗｓｋｉ圆盘定理与Ｂｒａｕｅｒ卵形定理得到了点格神经网络的完全稳定性条件，这为非对称 模板的点格神经网络提出一和中新的研究方法。     

CNN-Based Hybrid-Order Texture Segregation as Early Vision Processing and Its Implementation on CNN-UM

In this paper, a biologically inspired, CNN-based, multi-channel, texture boundary detection technique is presented. The proposed approach is similar to human vision system. The algorithm is simple and straightforward such that it can be implemented on the cellular neural networks (CNNs). CNN contains several important advantages, such as efficient real-time processing capability and feasible very large-scale integration (VLSI) implementation. The proposed algorithm also had been widely tested on synthetic texture images. Those texture images are randomly selected from the Brodatz textures database (1966). According to our simulation results, the boundaries of uniform textures can be detected quite successfully. For the nonuniform or nonregular textures, the results also indicate meaningful properties, and the properties also are consistent to the human visual sensation. The proposed algorithm also has been implemented on the CNN universal machine (CNN-UM), and yields similar results as the simulation on the PC. Based on the efficient performance of CNN-UM, the algorithm becomes very fast.     

The use of artificial neural networks for optimal message routing

Artificial neural networks are being designed to exploit the unique computational power of the human brain. A brief review of neural networks and their applications in communications is presented. Following that, the neural network solutions to the routing problems are given. Simulation results and performance comparisons are discussed. A comparison between the neural approach and other popular routing algorithms such as Bellman-Ford's and Dijkstra's algorithms is then presented. The practical significance of this new routing algorithm is discussed and further research work is suggested     

关于单体模糊神经网络感知机收敛定理的讨论

考虑单体模糊神经网络模型,讨论了单体模糊神经网络的感知机收敛定理与传统神经网络感知机收敛定理的关系,指出了在证明单体模糊神经网络的感知机收敛定理时存在的问题,提出了两种修改方案,即给出了两个改进的感知机收敛定理,并给出相应的证明和说明.     

基于正交校正共轭梯度法的快速神经网络学习算法研究

前馈神经网络由于具有理论上逼近任意非线性连续映射的能力,因而非常适合于非线性系统建模及构成自适应控制。为了提高前馈神经网络的权的学习效率及稳定性,该文提出一种基于正交校正共轭梯度优化方法的快速神经网络学习算法,通过与其它学习算法(如:BP算法、变尺度法、用差商近似代替导数的Powell法等)的比较,经仿真试验表明,本算法是一种高效、快速的学习算法。     

Classification of multisensor remote-sensing images by structuredneural networks

Proposes the application of structured neural networks to classification of multisensor remote-sensing images. The purpose of the approach is to allow the interpretation of the “network behavior”, as it can be utilized by photointerpreters for the validation of the neural classifier. In addition, this approach gives a criterion for defining the network architecture, so avoiding the classical trial-and-error process. First of all, the architecture of structured multilayer feedforward networks is tailored to a multisensor classification problem. Then, such networks are trained to solve the problem by the error backpropagation algorithm. Finally, they are transformed into equivalent networks to obtain a simplified representation. The resulting equivalent networks may be interpreted as a hierarchical arrangement of “committees” that accomplish the classification task by checking on a set of explicit constraints on input data. Experimental results on a multisensor (optical and SAR) data set are described in terms of both classification accuracy and network interpretation. Comparisons with fully connected neural networks and with the k-nearest neighbor classifier are also made     

Analog circuits for modeling biological neural networks: design andapplications

Computational neuroscience is emerging as a new approach in biological neural networks studies. In an attempt to contribute to this field, we present here a modeling work based on the implementation of biological neurons using specific analog integrated circuits. We first describe the mathematical basis of such models, then present analog emulations of different neurons. Each model is compared to its biological real counterpart as well as its numerical computation. Finally, we demonstrate the possible use of these analog models to interact dynamically with real cells through artificial synapses within hybrid networks. This method is currently used to explore neural networks dynamics.