Optimal design study of high order FIR digital filters based on neural network algorithm

An optimal design approach of high order FIR digital filter is developed based on the algorithm of neural networks with cosine basis function . The main idea is to minimize the sum of the square errors between the amplitude response of the desired FIR filter and that of the designed by training the weights of neural networks, then obtains the impulse response of FIR digital filter . The convergence theorem of the neural networks algorithm is presented and proved, and the optimal design method is introduced by designing four kinds of FIR digital filters , i.e., low-pass, high-pass, bandpass , and band-stop FIR digital filter. The results of the amplitude responses show that attenuation in stop-bands is more than 60 dB with no ripple and pulse existing in pass-bands, and cutoff frequency of passband and stop-band is easily controlled precisely .The presented optimal design approach of high order FIR digital filter is significantly effective.

Tower-Based Greenhouse Gas Measurement Network Design——The National Institute of Standards and Technology North East Corridor Testbed

The North-East Corridor （NEC） Testbed project is the 3rd of three NIST （National Institute of Standards and Technology） greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions （footprints）. Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

Design of complex FIR filters with arbitrary magnitude and group delay responses

To design approximately linear-phase complex coefficient finite impulse response （FIR） digital filters with arbitrary magnitude and group delay responses, a novel neural network approach is studied. The approach is based on a batch back-propagation neural network algorithm by directly minimizing the real magnitude error and phase error from the linear-phase to obtain the filter＇s coefficients. The approach can deal with both the real and complex coefficient FIR digital filters design problems. The main advantage of the proposed design method is the significant reduction in the group delay error. The effectiveness of the proposed method is illustrated with two optimal design examples.

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.

计及等效负载变化的消弧逆变器性能分析与设计

分析了消弧逆变器等效负载的性质,结果表明:等效负载是有源负载,等效源具有较大的幅值和较宽的变化范围;随过渡电阻和故障距离的变化,等效负载阻抗可呈现阻性、阻感性和阻容性。这一结果表明逆变器需要在负载性质改变和等效电源扰动的情况下,仍能稳定跟踪包含多频次的给定电流。对抗扰性和跟踪性要求的提高,增加了设计难度。鉴于此,提出了消弧逆变器输出滤波器和控制器的设计方法。输出滤波器采用滤波电容分流法进行设计,保证了逆变器能输出包含多频次的电流。采用电容串联电阻来抑制阻感负载情况下带来的谐振失稳问题,并给出了串联电阻的计算方法。控制器采用了定带宽设计法,带宽选定在幅频特性重合段。控制器参数设计准则为:保证阻容负载情况下传函幅频特性低频段具有较高增益。采用PSCAD/EMTDC仿真验证了结论的正确性和所提设计方法的可行性。

机器视觉中小目标检测实验优化模型设计与实现

目标检测是计算机视觉课程的重要实验内容之一,但现有模型对小目标检测能力普遍较弱。为了加深学生对现有模型结构和缺陷的深入理解,掌握模型的优化方法,基于实验中常用的SSD模型,引入了轻量级秩扩展网络ReXNet,重新设计了特征融合与过滤模块。特征融合模块在深浅层特征融合之前,先对深层特征图进行特征抽取,减少无效语义信息对浅层特征的干扰,增强了模型对小目标语义特征的表征能力。特征过滤模块则分别在分类和回归时,引入通道注意力和空间注意力的双路结构,提高分类与回归的精度。在VOC和COCO数据集上的实验结果表明,改进后的模型不仅提高了对小目标的检测性能,保留了较快的检测速度,而且改善了原始模型存在的漏检问题。通过模型设计的优化,加深了学生对于目标检测模型架构的理解,提高了学生的综合实践能力,促进了计算机视觉课程的实验教学内容建设。

Neural Network Algorithm for Designing FIR Filters Utilizing Frequency-Response Masking Technique

This paper presents a new joint optimization method for the design of sharp linear-phase finite-impulse response （FIR） digital filters which are synthesized by using basic and multistage frequency-response-masking （FRM） techniques. The method is based on a batch back-propagation neural network algorithm with a variable learning rate mode. We propose the following two-step optimization technique in order to reduce the complexity. At the first step, an initial FRM filter is designed by alternately optimizing the subfilters. At the second step, this solution is then used as a start-up solution to further optimization. The further optimization problem is highly nonlinear with respect to the coefficients of all the subfilters. Therefore, it is decomposed into several linear neural network optimization problems. Some examples from the literature are given, and the results show that the proposed algorithm can design better FRM filters than several existing methods.

基于主配网一体化的综合性电网仿真平台设计

国家电网公司提出了构建双碳背景下新型电力系统建设,主配网一体化调度支撑了省级电网全电压、扁平化电网调控业务发展,为新型电力系统安全高效运行提供坚强保障。文章提出了基于主配网一体化的综合性仿真电网平台建设方案,为省级培训中心主网调度、配网调度、变电运维等方向实训仿真系统提供准确有效地数据驱动。

基于无人机LiDAR数据的陡崖反坡滤波技术及应用

为了解决传统的不规则三角网(Triangular irregular network,TIN)算法在陡崖反坡地形滤波时,由于陡崖反坡会产生多个投影交点,坡度出现负值,陡崖反坡区域岩壁点往往被错误过滤的问题,提出一种刚性坐标变换与TIN结合的机载激光雷达(Light detection and ranging,LiDAR)点云滤波算法。采用刚性坐标变换的方法重新构建陡崖面的直观形态,将陡崖反坡等特异地形常规化。以湖北省恩施市姚家平水利枢纽工程坝址区域的陡崖反坡数据进行滤波分析,构建了侧向数字高程模型(DEM)表达陡崖地形,实现了侧向DEM在坝体填筑量计算上的应用。结果表明:该算法能够在陡崖反坡区域大幅降低Ⅰ类误差,并将Ⅱ类误差控制在一定范围内;应用该技术可为大坝设计提供直观准确的DEM,为三维设计提供了新思路。

CMOS无线体域网收发机集成电路滤波方法研究

电流波动是在电路中经常会遇到的问题,特别是在移动设备和传感器等应用中,电路的稳定性对设备运行的稳定性和可靠性有直接的影响。为了更好的降低电流波动,提出CMOS无线体域网收发机集成电路滤波仿真。构建互补金属氧化物半导体(Complementary Metal-Oxide-Semiconductor,CMOS)无线体域网收发机集成电路模型,通过分离集成电路中有功和无功电流的方法检测电路谐波,根据谐波特点实现滤波器的设计;基于滤波器设计原理、截止频率范围、范数、负荷容量以及稳定性,完成滤波器的设计,达到集成电流滤波仿真的目的。实验结果表明,所提方法对于CMOS无线体域网收发机集成电路中的电流波动滤波效果好。

电流模式N阶CCCII(±)-C低通滤波器的系统设计

提出了一种基于多输出电流控制传送器 (multiple outputs current controlled conveyorII,CCCII± )的多环反馈电流模式n阶低通滤波器的系统设计方法 .用该方法可产生出多种不同结构的n阶低通滤波器 .所有n阶滤波器均由 n个CCCII±及n个接地电容组成 .面向实际电路 ,完成了PSpice仿真 。

高阶滤波器最佳设计与实现

介绍设计高阶滤波器的一种有效方法．此方法是根据给定的指标要求，任选基本节来建立数学模型，继而确定选代公式，进行迭代，最终构成所需网络．这一方法简便、实用、电路类型少，经计算机对实例进行模拟，所得结果精确地逼近要求特性．

一种基于神经网络的形态滤波器优化设计方法

给出了一种利用神经网络实现灰度形态滤波器参数的优化设计方法。文中首先简述了形态滤波器的基本理论,并设计出了灰度形态学的两种最基本运算(膨胀和腐蚀)的神经网络结构,其网络权值即为形态运算的结构元素值。然后,按照d学习规则,自适应地对结构元素(形状和数值)进行学习训练,以获得最佳的灰度形态滤波器参数。该方法设计简便,实用性强且易于推广,对提高形态滤波性能效果显著。

高阶多通带滤波器优化设计研究

本文详细讨论了FIR线性相位滤波器幅频特性与余弦基神经网络算法之间的关系 ,证明了神经网络的收敛定理 ,给出了高阶多通带滤波器的设计实例 .

图像滤波的形态学开、闭型神经网络算法

该文设计完成了一种具有实用意义的形态学开、闭滤波的神经网络模型及其滤波参数的优化训练算法。实验结果表明该方法设计简便,实用性强且易于推广,对提高形态滤波性能效果明显。分析表明,形态滤波器可分解为形态滤波运算和结构元素选择两个基本问题。形态滤波运算规则已由定义本身确定,于是形态滤波器的最终滤波性能就仅仅取决于结构元素的选择。进行自适应优化训练的目的正是使结构元素具有图像目标的形态结构特征,从而使形态滤波器对复杂变化的图像具有良好的滤波性能和稳健的适应能力。

基于PSO的无源电力滤波器支路拓扑的优化设计

无源电力滤波器(Passive Power Filter,PPF)以其高性价比在电力系统中得到了广泛的应用。用不同阻抗频率特性的PPF拓扑支路组成无源滤波网络是目前无源滤波的通用方法,实际中PPF支路拓扑设计通常依靠设计者的工程经验,缺少系统的设计方法,更谈不上对支路拓扑的优化设计。本研究提出了一种基于粒子群优化算法(Particle Swarm Optimization,PSO)的PPF支路拓扑的优化设计方法,解决了工程设计中单纯依靠工程经验设计PPF支路拓扑的盲目性问题。本方法首先基于PSO优化了各种滤波支路中补偿容量的分配,然后以总投资成本、单调谐滤波支路平均品质因数及电流谐波平均含有率为目标函数优化出最佳PPF滤波支路网络。并以一个实际的工况为例,验证了优化设计效果。

神经网络在4型FIR滤波器的优化设计研究

本文详细讨论了4型线性相位滤波器幅频特性与正弦基神经网络算法间的关系,提出并证明了该算法的收敛定理,给出了FIR高通滤波器和FIR带通滤波器优化设计实例。计算机仿真结果表明,该算法在FIR高通滤波器和带通滤波器的有效性和优异性能。

随机系统中能容忍连续丢包和测量时延的卡尔曼滤波(英文)

通过转换原线性系统到能容忍连续丢包和测量时延的随机参数系统,推导了一个最优线性滤波器.给出一个仿真例子,比较已存在的结果,仿真结果表明被提出的线性滤波器有优越的性能.然而,该滤波器不能应用于非线性系统.从应用角度,为非线性系统提出了一个增强型的滤波器.而且,该增强型的滤波器能成功地应用于不可靠的无线传感器网络场景来跟踪移动目标.这些滤波器只依靠测量值的达到概率,而不需要知道某一时刻测量是否接收.仿真说明了被提出的增强型滤波器不仅能改善实时目标跟踪的鲁棒性,而且比标准的扩展卡尔曼滤波器能够提供更精确的估计.

开源设计社区中成员结构对知识协作网络进化的影响

为探索开源设计社区中不同成员构成对知识协作网络进化的影响,建立了开源设计社区知识协作网络进化过程模型,刻画了主体—任务知识匹配机制、协作伙伴选择机制、知识溢出机制和知识增长机制。使用多主体建模及仿真方法实现了开源设计知识协作网络进化过程的仿真。设计了一系列仿真实验,研究了不同数量比例的成员结构对知识协作网络进化的影响。实验结果表明,增加精英主体的比例能够显著提高项目的成长速度,但随着精英主体的增加,精英主体带来的项目成长速度与知识增长效应逐渐衰弱。基于仿真结果提出了开源设计社区的管理建议。