One high-efficiency analysis method for high-speed circuit networks containing distributed parameter elements

In the field of high-speed circuits, the analysis of mixed circuit networks containing both distributed parameter elements and lumped parameter elements becomes ever important. This paper presents a new method for analyzing mixed circuit networks. It adds transmission line end currents to the circuit variables of the classical modified nodal approach and can be applied directly to the mixed circuit networks. We also introduce a frequency-domain technique without requiring decoupling for multiconductor transmission lines. The two methods are combined together to efficiently analyze high-speed circuit networks containing uniform,nonuniform,and frequency-dependent transmission lines. Numerical experiment is presented and the results are compared with that computed by PSPICE.

Fractional-order L_βC_a filter circuit network

In this paper we introduce the new fundamentals of the conventional LC filter circuit network in the fractional domain.First, we derive the general formulae of the impedances for the conventional and fractional-order filter circuit network.Based on this, the impedance characteristics and phase characteristics with respect to the system variables of the filter circuit network are studied in detail, which shows the greater flexibility of the fractional-order filter circuit network in design.Moreover, from the point of view of the filtering property, we systematically study the effects of the filter units and fractional orders on the amplitude–frequency characteristics and phase–frequency characteristics. In addition, numerical tables of the cut-off frequency are presented. Finally, two typical examples are presented to promote the industrial applications of the fractional-order filter circuit network. Numerical simulations are presented to verify the theoretical results introduced in this paper.

Sensitivity analysis of distributed parameter elements in high-speed circuit networks

This paper presents an analysis method, based on MacCormack＇s technique, for the evaluation of the time domain sensitivity of distributed parameter elements in high-speed circuit networks. Sensitivities can be calculated from electrical and physical parameters of the distributed parameter elements. The proposed method is a direct numerical method of time-space discretization and does not require complicated mathematical deductive process. Therefore, it is very convenient to program this method. It can be applied to sensitivity analysis of general transmission lines in linear or nonlinear circuit networks. The proposed method is second-order-accurate. Numerical experiment is presented to demonstrate its accuracy and efficiency.

Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.

ROUTE SELECTION IN VIRTUAL CIRCUIT DATA NETWORKS BASED ON GENETIC ALGORITHMS

This paper addresses the problem of selecting a route for every pair of communicating nodes in a virtual circuit data network in order to minimize the average delay encountered by messages. The problem was previously modeled as a network of M/M/1 queues. Agenetic algorithm to solve this problem is presented. Extensive computational results across a variety of networks are reported. These results indicate that the presented solution procedure outperforms the other methods in the literature and is effective for a wide range of traffic loads.

Improved RBF network application in analog circuit fault isolation

One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorithm simplified the structure of network through optimum output layer coefficient with incremental projection learning(IPL)algorithm,and adjusted the parameters of the neural activation function to control the network scale and improve the network approximation ability.Compared to the traditional algorithm,the improved algorithm has quicker convergence rate and higher isolation precision.Simulation results show that this improved RBF network has much better performance,which can be used in analog circuit fault isolation field.

基于MSPA-MCR-CIRCUIT的山西省运城市景观生态网络构建

山西省运城市位于黄河中下游三省交界处,其景观要素成分复杂,对运城景观生态网络斑块与廊道的研究有利于黄河生态系统稳定性的保护。基于形态空间格局理论(MSPA)确定连通性强的核心斑块,识别核心生态源地,以最小累计阻力模型(MCR)为基础,叠加7种阻力因子构建综合阻力面,根据重力模型划分生态廊道等级。以基于电路理论(CIRCUIT)的Linkage Mapper工具识别生态夹点和障碍点作为生态节点,综合构建源地(面)-廊道(线)-节点(点)的运城市景观生态网络。结果表明:1)运城市核心生态源地有9处,综合重力模型和实际建设需求划分13条重要生态廊道和10条一般生态廊道,识别出关键生态节点17个,一般生态节点34个;2)现有重要生态廊道集中于运城南部,南北方向生态联系较弱,可优先加强贯穿稷山县、新绛县、闻喜县的重要生态廊道建设;3)MSPA模型与电路理论的综合运用可提升景观生态网络构建的科学性和准确性,有利于确定生态修复关键区域,为生态修复项目的布局提供有力支撑。

DECISION OF OPTIMAL HOPFIELD NEURAL NETWORK MODEL OF COMBINATIONAL CIRCUITS

By use of Hopfield model and basis solution of homogeneous linear equations which are established in accordance with consistent state, a practical decision method for the existence of optimal Hopfield model of combinational circuits is provided. Finally, an example is given.

Deep Neural Network Based Behavioral Model of Nonlinear Circuits

With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.

ANALOG CIRCUIT IMPLEMENTATION OF NEURAL NETWORK WITH HIGH PRECISION WEIGHTS

A current-mode MOS neuron circuit with 4-bit programmable weights is presented by using CMOS technology. The weights of the neurcn have high resolution and also can easily be digitally stored. The resolution can be extended into high levels such as 8-bit, etc. by the design methodology presented in this paper. The operational principle of the neuron is discussed. Circuit simulation has been made by use of SPICE II. The results give a good agreement for the design requirements.

Analog-Circuit Model of FGH96 Superalloy Hot Deformation Behaviors Based on Artificial Neural Network

At the present time, numerical models （such as, numerical simulation based on FEM） adopted broadly in technological design and process control in forging field can not implement the realtime control of material forming process. It is thus necessary to establish a dynamic model fitting for the real-time control of material deformation processing in order to increase production efficiency, improve forging qualities and increase yields. In this paper, hot deformation behaviors of FGH96 superalloy are characterized by using hot compressive simulation experiments. The artificial neural network （ANN） model of FGH96 superalloy during hot deformation is established by using back propagation （BP） network. Then according to electrical analogy theory, its analog-circuit （AC） model is obtained through mapping the ANN model into analog circuit. Testing results show that the ANN model and the AC model of FGH96 superalloy hot deformation behaviors possess high predictive precisions and can well describe the superalloy＇s dynamic flow behaviors. The ideas proposed in this paper can be applied in the real-time control of material deformation processing.

Fault Diagnosis of Analog Circuit Based on PSO and BP Neural Network

In order to improve the speed and accuracy of analog circuit fault diagnosis,using Back Propagation Neural Network(BPNN),a new method is proposed based on Particle Swarm Optimization(PSO)to adjust weights of BP neural network.The model can not only overcome the limitations of the slow convergence and the local extreme values by basic BP algorithm,but also improve the learning ability and generalization ability with a higher precision.The response signals of analog circuit is preprocessed by Wavelet Packet Transform(WPT)as the fault feature.The simulation result shows that the proposed method has higher diagnostic accuracy and faster convergence speed,which is effective for fault location.

Modeling of the Coupling of the Lightning Shock Wave Flow Circuit in the High Voltage Electrical Network

This article focuses on the aggression of lightning overload on the equipment of the electrical network of sites where storm activity is very dense;and the electrocution of people located in the direct environment of the high-voltage substation during the flow of lightning current to the ground through the ground socket. The modeling of the flow circuit of the shock wave consisting of guard wire, lightning arrester and ground socket couple to the transformer of the high voltage substations, thanks to the approach of a servo block, led to the synthesis of a PID regulator (corrector) whose action is to reject the effects of the overvoltage on the network equipment and to significantly reduce or even cancel the effects of the step or touch voltage due to the distribution of the potential around the ground socket;and thus improve the quality of service of the high-voltage transmission and distribution electricity network, especially in stormy times.

Circuit Design of On- Chip BP Learning Neural Networkwith Programmable Neuron Characteristics

A circuit system of on\|chip BP(Back\|Propagation) learning neural network with programmable neurons has been designed,which comprises a feedforward network,a n error back\|propagation network and a weight updating circuit.It has the merit s of simplicity,programmability,speediness,low power\|consumption and high densi ty.A novel neuron circuit with programmable parameters has been proposed.It gene rates not only the sigmoidal function but also its derivative.HSPICE simulations are done to a neuron circuit with level 47 transistor models as a standard 1 2 μm CMOS process.The results show that both functions are matched with their res pective ideal functions very well.The non\|linear partition problem is used to v erify the operation of the network.The simulation result shows the superior perf ormance of this BP neural network with on\|chip learning.

Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.

基于电路理论的苏南水网地区绿色基础设施网络构建——以苏州南星湖片区为例

随着新型城镇化的推进,建设用地扩张迅速,苏南水网地区面临生境破碎与生态退化等问题,构建绿色基础设施网络是整合破碎生境斑块、提升生态系统稳定性和整体性的有效措施。本研究以苏州吴江区东北部南星湖片区为例,基于“网络中心-连接廊道-战略点”的构建模式,运用MSPA、InVEST模型与景观连通性提取网络中心,结合电路理论提取连接廊道、战略点,构建水网地区绿色基础设施网络,共识别网络中心18处,以湖泊、林地生态用地为主,呈现东部密集西部稀疏的分布特征;提取连接廊道35条,空间分布呈现北部较为密集、南部相对稀疏的特征;选取夹点和障碍点2种战略点共16处,包括湿地、耕地等用地类型,在同里湖、石头潭附近分布较多。研究综合考虑景观要素的生态服务重要性和生态功能连通性构建水网地区绿色基础设施网络,对于改善区域生态环境质量与促进生态系统稳定与可持续发展具有积极意义。

新型电缆贯通供电系统短路特性分析

新型电缆贯通供电系统能够实现长距离输电、减少电分相数目,但两级供电模式将导致该系统结构复杂.为研究电缆贯通供电系统短路特性,首先,建立单位长度的牵引电缆和接触网-钢轨的二端口网络参数,将各个子网络级联等效成1个二端口网络,进而转化为Ⅱ型电路,实现牵引电缆和接触网-钢轨任意长度的分布参数建模;由于接触网短路后导致机车电压下降,基于车网耦合关系采用迭代计算求解短路电流电压,仿真验证计算方法的准确性.最后,解析不同短路类型的电气特性,重点分析分布电容对短路电流、电压的影响.研究结果表明:分布电容会引起短路电流增大,且在不同短路情况下均造成牵引变压器输出电压增加;接触网短路电流由两侧牵引变压器共同提供,距短路点越远提供的电流越少;电缆相间短路对非故障回路机车运行影响最大.

基于语义加权网络的重点领域科学基金资助态势识别研究

[目的/意义]科学基金制度是各国(地区)提升科学研究水平的重要方式,准确识别科学基金的资助方向和资助成效是感知科技发展态势的关键。[方法/过程]为识别科学基金资助态势,在提出关键短语抽取规则后,利用语义相似度消除同、近义词的歧义影响,利用动态滑动窗口和语义相似度构建语义加权的词共现网络,并基于社团划分方法识别基金主题。在集成电路领域比较1812项中国国家自然科学基金和2807项美国国家科学基金的资助主题分布、资助力度变化和资助效果,该方法能够准确识别基金主题分布和资助成效。[结果/结论]中国国家自然科学基金和美国国家科学基金均涵盖了集成电路领域的主要研究主题,在重点资助方向和资助力度上有较大差异;中国国家自然科学基金资助的论文平均被引频次相对较低,受资助机构相对单一。

武汉城市圈“生物迁移-区域降温”复合功能生态网络构建

[目的]生态网络是大尺度景观保护的有效空间途径,既有研究所构建的生态网络多侧重于生物多样性保护,对更多潜在的效益考虑不足,因此亟须拓展生态网络的更多功能。[方法]以武汉城市圈为例,综合地表温度反演、形态学空间格局分析、景观连通性分析和电路理论等方法,协同构建生物迁移网络与区域降温网络,并叠合生成复合功能生态网络格局,将识别出的双网络交点、夹点和障碍点作为生态保护与修复的重点区域。[结果]1)研究区共有生物多样性源地22个、区域降温源地27个,二者主要分布于研究区东北部和南部;2)识别出生物迁移廊道44条(重要廊道21条)、区域降温廊道51条(重要廊道29条);3)叠加分析发现共有8个源地斑块承担复合功能;4)复合功能视角下的生态保护与修复重点区域包含双网络交点7处,生物迁移网络夹点面积85.4 km^(2)、障碍点面积724.9 km^(2),区域降温网络夹点面积50.1 km^(2)、障碍点面积926.6 km^(2)。[结论]双网络耦合形成复合功能生态网络,协同增益效果体现为网络功能拓展与结构韧性提升。武汉城市圈应从现有源地保护与潜在源地培育、双网络障碍点修复、双网络夹点与交点保护3个方面进行系统性、全局性的生态保护和修复工作。

基于多特征融合和深度信念网络的断路器元件故障识别研究

为了标准化断路器元件故障识别与诊断流程,提高断路器运行的稳定性,引进多特征融合技术和深度信念网络,研究设计断路器元件的故障识别方法。同步布置传感器,建立传感器与终端计算机之间的通道,进行断路器元件运行信号采集与多特征深度融合;为排除其他因素造成的振动信号,实现对断路器元件故障的准确识别,引进深度信念网络,进行断路器元件振动信号的重构;将重构后的微调整信号作为核心,通过对其时域与频域的分析,进行断路器元件故障的识别与分类。对比实验结果证明:设计的方法可以实现对断路器元件故障的诊断与故障类型的精准识别,应用效果良好。