基于中值滤波的红外焦平面阵列非均匀性神经网络校正

统的神经网络校正算法存在收敛速度慢和校正精度低的缺点。当背景噪声较大时,它更难以获得令人满意的校正效果。 针对其不足之处, 提出一种基于中值滤波的红外焦平面阵列(IRFPA)非均匀性神经网络校正算法。该算法首先利用中值滤波对强噪声进行预处理,在此基础上 采用改进的神经网络校正算法对IRFPA非均匀性进行自适应校正。实验结果表明,该算法与传统的神经网络方法相比具有收敛速度快和校正精 度高等特点,并且使图像的峰值信噪比至少提高了10dB。     

基于APSO的盲源分离算法在盲图像分离中的应用

利用独立分量分析(ICA)的自适应粒子群(APSO)算法对因传输等过程而引起的多幅灰度图像混叠进行盲分离,针对图像盲分离提出了一种基于改进的APSO的盲源分离算法并将其应用于分离模糊灰度图像。利用峰度和负熵分别作为粒子群算法的第一和第二适应度函数根据其高斯性原理作为独立性判别标准对分离矩阵进行自适应更新。分析比较不同盲分离算法对图像分离的收敛性,仿真结果证明改进的自适应粒子群算法能够很好地分离图像且计算性能指标优越,收敛效果好。     

Pilot-based joint ML estimation of timing offset,CFO and channel for OFDMA uplink using minimum residue decomposition technique

In this paper, a uniform circular antenna array (UCAA) combining genetic algorithm (GA) or asynchronous particle swarm (APSO) for finding out global maximum of multi-objective function in indoor ultra-wideband (UWB) communication system is proposed. The algorithm is used to synthesize the radiation pattern of the directional UCAA to reduce the bit error rate (BER), to increase received energy and channel capacity in indoor UWB communication system. Using the impulse response of multipath channel, the BER of the synthesized antenna pattern on binary antipodal-pulse amplitude modulation system can be calculated. Based on topography of the antenna and the shooting and bouncing ray/image techniques, the synthesized problem can be reformulated into a multi-objective optimization problem which would be solved by the GA and APSO. Numerical results show that the fitness value and convergence speed by APSO is better than those by GA. The results also show that for multi-objective problem APSO compared to GA can reduce the BER substantially. Moreover, APSO can get better results for both line-of-sight and non line-of-sight cases.     

一种基于互相关神经网络的声呐盲波束形成方法研究

针对水声环境和水声信号的特点，提出了一种基于神经网络的声呐盲波束形成算法。该方法利用水声信号的循环平稳特性把波束形成权向量的求解问题转化为阵列接收信号互相关函数的奇异值分解问题；引入一种互相关神经网络求解阵列接收信号相关函数的奇异值，从而减小了运算的代价，可高效实现盲波束形成。提出的改进互耦Hebbian学习规则有效地提高了神经网络权值的更新速度，为问题的实时求解提供了有效的途径。该方法还能抑制噪声和干扰的影响，表现出较强的顽健性。仿真实验验证了算法的正确性。     

基于EXIT图和自适应微粒群算法的度分布对优化方法

综合EXIT图法和自适应微粒群优化(APSO)算法的优点,该文提出了一种基于EXIT图和APSO算法的非正则LDPC码度分布对优化方法。该方法设计了衡量EXIT曲线匹配程度的全局代价函数,并运用APSO算法对度分布对进行快速迭代优化,迭代过程中不需要固定CND曲线,可以获得EXIT曲线更加匹配的优化度分布对,以及更高的噪声门限。仿真结果表明,该方法在码结构优化方面有着很好的性能,且优化速度较高斯逼近法有了较大提高。     

Sufficient and necessary conditions for global exponential stability of discrete-time recurrent neural networks

A set of sufficient and necessary conditions are presented for global exponential stability (GES) of a class of generic discrete-time recurrent neural networks. By means of the uncovered conditions, GES and convergence properties of the neural networks are analyzed quantitatively. It is shown that exact equivalences exist among the GES property of the neural networks, the contractiveness of the deduced nonlinear operators, and the global asymptotic stability (GAS) of the neural networks plus the spectral radius of Jacobian matrix of the neural networks at the unique equilibrium point less than one. When the neural networks have small state feedback coefficients, it is shown further that the infimum of exponential bounds of the trajectories of the neural networks equals exactly the spectral radius of Jacobian matrix of the neural networks at the unique equilibrium point. The obtained results are helpful in understanding essence of GES and clarifying difference between GES and GAS of the discrete-time recurrent neural networks.     

基于级联随机共振与APSO算法相结合的信号检测方法

针对随机共振方法以系统的参数和噪声强度的匹配为研究背景的局限性,为解决级联双稳系统参数的合理选取的问题及克服自适应随机共振单参数优化的不足之处,提出了一种基于级联随机共振与自适应粒子群(APSO)算法相结合的方法。该方法以系统的输出信噪比为优化目标函数,采用自适应粒子群算法较强的全局搜索能力和粒子(待优化参数)的多样性,对级联双稳态随机共振的级联系统参数进行同步优化,使系统处于最佳随机共振工作状态。仿真结果表明,该方法可以提高输出信噪比及算法的收敛速度,实现良好的检测效果。     

解L1范数极小化问题的神经网络

本文提出了一个求解Ｌ１范数极小化问题的神经网络新模型，并予以严格证明，对比文［１］中的模型，新模型具有较小的规模；对比文［２］中的模型，新模型不含惩罚参数，因而具有全局收敛到精确解等优点，最后，模拟试验表明，新模型在离散时间情形也是全局收敛的。     

离散时间点格神经网络的开关电流电路实现

本文将原连续时间电压域点格神经网络（CNN）模型[1]映射到离散时间电流域，并采用开关电流（SI）技术来进行离散时间CNN的VLSI设计。给出了基本积木块的实现电路，并进行了连通片检测方面的简单应用研究。计算机仿真与理论结果相吻合，所得电路非常适合于用标准CMOS工艺集成，具有潜在的应用前景。     

Systolic array algorithm for the Hopfield neural network guaranteeing convergence

It has been frequently reported that the Hopfield neural network operating in discrete-time and parallel update mode will not converge to a stable state, which inhibits the parallel execution of the model. The authors propose a systolic array algorithm for the parallel simulation of the Hopfield neural network which guarantees the convergence of the network and achieves linear speedup as the number of processors is increased.<>     

From Zhang Neural Network to Newton Iteration for Matrix Inversion

Different from gradient-based neural networks, a special kind of recurrent neural network (RNN) has recently been proposed by Zhang for online matrix inversion. Such an RNN is designed based on a matrix-valued error function instead of a scalar-valued error function. In addition, it was depicted in an implicit dynamics instead of an explicit dynamics. In this paper, we develop and investigate a discrete-time model of Zhang neural network (termed as such and abbreviated as ZNN for presentation convenience), which is depicted by a system of difference equations. Comparing with Newton iteration for matrix inversion, we find that the discrete-time ZNN model incorporates Newton iteration as its special case. Noticing this relation, we perform numerical comparisons on different situations of using ZNN and Newton iteration for matrix inversion. Different kinds of activation functions and different step-size values are examined for superior convergence and better stability of ZNN. Numerical examples demonstrate the efficacy of both ZNN and Newton iteration for online matrix inversion.      

Nonlinear digital filters mimicking cellular neural networks

This work is motivated by the need forFaithful digital simulation of cellular neural networks (CNNs) that maintains most of their qualitative properties of stability and convergence. An interconnection of nonlinear digital filters mimicking behaviors of the analog CNNs is proposed, and the main properties are studied in detail. The discrete model obtained is proven to have the same convergence properties as the original analog network. The key to this development is the use of anAppropriate discretization scheme. Our discrete approximation to the nonlinear state-space representation of cellular neural networks is such that the Lyapunov function used to show convergence in analog cellular neural networks is still a Lyapunov function (when appropriately discretized) for our nonlinear digital filter network. This is in contrast to other digital simulations of CNNs, which have not been proven to preserve the convergence properties. The network of nonlinear digital filters so introduced thus adds another item to the catalog of digital filters obtained viaappropriate discretization of analog circuits, e.g., wave digital filters, orthogonal filters, and certain other of their more recently studied nonlinear counterparts.All authors were with the Stevens Institute of Technology, Hoboken, NJ, 07670 when this work was performed.Support from NSF grant MIP 9696176.     

基于遗传算法优化BP神经网络的可见光定位

采用接收信号强度(RSS)方法的室内可见光定位 ,因受多径效应及噪声的影响,对距离估计不准确, 定位精度不高。为提高定位精度,本文提出了一种采用遗传算法优化BP神经网络(GA-BP) 的距离估计方法。 先通过遗传算法优化BP神经网络的初始权值,经过优化后的BP神经网络收敛速度快,不易 限于局部最优。 再利用GA-BP神经网络对收发端之间的距离进行修正,使其接近于真实距离。最后使用最 小二乘法解算待 定位点坐标,同时在不同定位范围和不同定位位置下,与传统RSS加权质心方法的可见光定 位结果进行对 比。仿真结果表明,在5m×5m×3m的定位场景中,平均定位误差可以达到0.642 cm。与传统RSS加权质 心方法相比,平均定位精度提高了约96.4%。且在不同定位范围和不 同定位位置下,平均定位误差稳定在 毫米级,尤其不随定位范围的扩大而扩大。有效地提高了室内定位精度和系统应用的普适性 。     

Enhancing Design of Visual-Servo Delayed System

A robust adaptive predictor is proposed to solve the time-varying and delay control problem of an overhead crane system with a stereo-vision servo. The predictor is based on the use of a recurrent neural network (RNN) with tapped delays, and is used to supply the real-time signal of the swing angle. There are two types of discrete-time controllers under investigation, i.e., the proportional-integral-derivative (PID) controller and the sliding controller. Firstly, a design principle of the neural predictor is developed to guarantee the convergence of its swing angle estimation. Then, an improved version of the particle swarm optimization algorithm, the parallel particle swarm optimization (PPSO) method is used to optimize the control parameters of these two types of controllers. Finally, a homemade overhead crane system equipped with the Kinect sensor for the visual servo is used to verify the proposed scheme. Experimental results demonstrate the effectiveness of the approach, which also show the parameter convergence in the predictor.     

Adaptive estimation in linear systems with unknown Markovian noise statistics

The asymptotic behavior of a Bayes optimal adaptive estimation scheme for a linear discrete-time dynamical system with unknown Markovian noise statistics is investigated. Noise influencing the state equation and the measurement equation is assumed to come from a group of Gaussian distributions having different means and covariances, with transitions from one noise source to another determined by a Markov transition matrix. The transition probability matrix is unknown and can take values only from a finite set. An example is simulated to illustrate the convergence.     

Discrete-time cellular neural networks using distributed arithmetic

An efficient digital architecture for the discrete-time cellular neural networks (DTCNNs) is proposed that employs the distributed arithmetic (DA). It consumes little silicon area because of the bit serial computation of DA, and offers higher speed operation than the analogue implementations of DTCNN. The proposed architecture has been implemented in a 0.8 μm CMOS technology     

一种基于改进暂态混沌神经网络的信道分配算法

该文针对暂态混沌神经网络(TCNN)求解信道分配问题(CAP),分析混沌神经网络模型及其混沌性态,依据其按自反馈连接权值的减小,由混沌态通过逆分岔而收敛到稳定状态的特性,提出了一种对暂态混沌神经网络进行分段退火的策略,即依据混沌神经网络运行过程中,对应Lyaponov指数的变化特性而确定分段点,使网络能有效地利用混沌态进行全局搜索和加快收敛;在7小区的信道分配中,网络收敛速度提升了30%左右,在25小区的Kunz基准测试程序的仿真中,收敛速度也提升了近15%;仿真结果表明其有效减少了网络运算的迭代步数,提高了网络的搜索效率;通过相应理论和仿真结果的分析,对网络的搜索性能、参数的选择与设置进行了进一步的讨论。     

Robust M-estimate adaptive filtering

An M-estimate adaptive filter for robust adaptive filtering in impulse noise is proposed. Instead of using the conventional least-square cost function, a new cost function based on an M-estimator is used to suppress the effect of impulse noise on the filter weights. The resulting optimal weight vector is governed by an M-estimate normal equation. A recursive least M-estimate (RLM) adaptive algorithm and a robust threshold estimation method are derived for solving this equation. The mean convergence performance of the proposed algorithm is also analysed using the modified Huber (1981) function (a simple but good approximation to the Hampel's three-parts-redescending M-estimate function) and the contaminated Gaussian noise model. Simulation results show that the proposed RLM algorithm has better performance than other recursive least squares (RLS) like algorithms under either a contaminated Gaussian or alpha-stable noise environment. The initial convergence, steady-state error, robustness to system change and computational complexity are also found to be comparable to the conventional RLS algorithm under Gaussian noise alone     

Global output convergence of a class of continuous-time recurrent neural networks with time-varying thresholds

This paper discusses the global output convergence of a class of continuous-time recurrent neural networks (RNNs) with globally Lipschitz continuous and monotone nondecreasing activation functions and locally Lipschitz continuous time-varying thresholds. We establish one sufficient condition to guarantee the global output convergence of this class of neural networks. The present result does not require symmetry in the connection weight matrix. The convergence result is useful in the design of recurrent neural networks with time-varying thresholds.     

基于自适应粒子群优化的RBF毫米波信道建模研究

基于毫米波室内无线信道测量数据,将机器学习(machine learning,ML)中的径向基函数(radial basis function,RBF)方法应用于毫米波信道建模中,建立了基于自适应粒子群优化(adaptive particle swarm optimization,APSO)的RBF神经网络信道参数预测...