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.

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.

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.

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。

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.

Wavelet neural network based fault diagnosis in nonlinear analog circuits

The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studied. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation(BP) neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

A new matrix-based mathematical model for determining unidirectional circuits in a ventilation network

The occurrence of local circulating ventilation can be caused by many factors, such as the airflow reversion during mine fire,the improper arrangement of local fan or underground fan station and the man-made error input of raw data before network solving. Once circulating ventilations occur,the corresponding branches in the ventilation network corresponding to the relevant airways in ventilation system form circuits,and all the direc- tions of the branches in the circuits are identical,which is the unidirectional problem in ventilation network.Based on the properties of node adjacent matrix,a serial of mathe- matical computation to node adjacent matrix were performed,and a mathematical model for determining unidirectional circuits based on node adjacent matrix was put forward.

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.

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.

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.

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.

Design and Analysis of a Network Traffic Analysis Tool: NetFlow Analyzer

A network analyzer can often comprehend many protocols, which enables it to display talks taking place between hosts over a network. A network analyzer analyzes the device or network response and measures for the operator to keep an eye on the network’s or object’s performance in an RF circuit. The purpose of the following research includes analyzing the capabilities of NetFlow analyzer to measure various parts, including filters, mixers, frequency sensitive networks, transistors, and other RF-based instruments. NetFlow Analyzer is a network traffic analyzer that measures the network parameters of electrical networks. Although there are other types of network parameter sets including Y, Z, & H-parameters, these instruments are typically employed to measure S-parameters since transmission & reflection of electrical networks are simple to calculate at high frequencies. These analyzers are widely employed to distinguish between two-port networks, including filters and amplifiers. By allowing the user to view the actual data that is sent over a network, packet by packet, a network analyzer informs you of what is happening there. Also, this research will contain the design model of NetFlow Analyzer that Measurements involving transmission and reflection use. Gain, insertion loss, and transmission coefficient are measured in transmission measurements, whereas return loss, reflection coefficient, impedance, and other variables are measured in reflection measurements. These analyzers’ operational frequencies vary from 1 Hz to 1.5 THz. These analyzers can also be used to examine stability in measurements of open loops, audio components, and ultrasonics.

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MSPA-CIRCUIT的长江中游城市群热环境网络识别与评价研究

快速城市化是热环境风险加剧的主因,从网络视角分析热环境的空间结构特征,对于改善热环境、增强城市可持续性以适应气候变化具有重要意义。以长江中游城市群为例,基于2000、2010及2020年地表温度数据与建成区面积数据分析城市群地表温度与热岛区域时空特征,采取形态学空间格局分析(MSPA)模型提取并划分热岛斑块类型,包括核心、孤岛、孔隙、边缘、环道、桥接以及支线,在此基础上构建热环境源地与热环境廊道,并利用电路(CIRCUIT)理论对城市群热环境网络进行识别。此外,依据多元指标分别评估城市群热环境网络的总体连通性(α指数、β指数与γ指数)与空间连通性(特征向量中心性与电流密度)。结果表明:(1)2000-2020年期间,长江中游城市群夏季地表温度高温区与次高温区比例整体呈上升趋势,常温区、低温区以及次低温区比例整体呈下降趋势,各类型热岛斑块的地表温度中位数呈现核心型热岛斑块最高而支线型热岛斑块最低的规律;(2)2000-2020年期间,长江中游城市群夏季热岛区域总面积由2.80×10^(3)km^(2)增至12.8×10^(3)km^(2),其中核心型热岛斑块面积占比由31.1%增加至45.9%,在空间上呈现逐渐向武汉、长沙以及南昌等地集聚的趋势;(3)2000-2020年期间,长江中游城市群热环境源地数量由56个增至215个,热环境廊道数量由89条增至378条,包含热环境廊道的城市比例由77.4%增至100%,已初步形成城市群全局热环境网络;(4)2000-2020年期间,长江中游城市群热环境网络的总体连通性呈逐渐增强态势,空间连通性的分布趋势由“南高北低”的空间异质性格局向“全局高连通性”的空间稳定性格局演变。该研究旨在为区域尺度的气候适应发展策略提供启示。