Nullors, and Nullor Circuits;There Applications in Symbolic Circuit Analysis and Design

The objective in this presentation is to introduce some of the unique properties and applications of nullors in active circuit analysis and designs. The emphasis is to discuss the role nullors can play in symbolic representation of transfer functions. To show this we adopt the topological platform for the circuit analysis and use a recently developed Admittance Method (AM) to achieve the Sum of Tree Products (STP), replacing the determinant and cofactors of the Nodal Admittance Matrix (NAM) of the circuit. To construct a transfer function, we start with a given active circuit and convert all its controlled sources and I/O-ports to nullors. Now, with a solid nullor circuit (passive elements and nullors) we first eliminate the passive elements through AM operations. This produces the STPs. Second, the all-nullor circuit is then used to find the signs or the STPs. Finally, the transfer function (in symbolic, if chosen) is obtained from the ratio between the STPs.

Modeling of the Earth’s Planetary Heat Balance with Electrical Circuit Analogy

The integral heat model for the system of the Earth’s surface—the atmosphere—the open space based on the electrical circuit analogy is presented. Mathematical models of the heat balance for this system are proposed. Heat circuit which is analog of the electrical circuit for investigating the temperature dependencies on the key parameters in the clear form is presented.

A Signal-Flow Driven Automatic Physical Synthesis Methodology for Analog Circuits

This paper introduces a novel automatic physical synthesis methodology for analog circuits based on the signal-flow analysis.Circuit analysis sub-system adopts the newly advanced methodology,circuit topology analysis,and circuit sensitivity analysis to generate layout constraints and control performance degradations.Considering the heuristic information about signal-flow,complexity of the methodology is less than the pure performance-driven methodology.And then these constraints are implemented in device generation,placement,and routing sub-systems separately,which makes the different constraints be satisfied at most easily implemented stages.Excellent circuit performance obtained by the methodology is demonstrated by practical circuit examples.

Analog circuit intelligent fault diagnosis based on GKPCA and multi-class SVM approach

Analog circuits fault diagnosis is essential for guaranteeing the reliability and maintainability of electronic systems. In this paper, a novel analog circuit fault diagnosis approach is proposed based on greedy kernel principal component analysis (KPCA) and one-against-all support vector machine (OAASVM). In order to obtain a successful SVM-based fault classifier, eliminating noise and extracting fault features are very important. Due to the better performance of nonlinear fault features extraction and noise elimination as compared with PCA, KPCA is adopted in the proposed approach. However, when we adopt KPCA to extract fault features of analog circuit, a drawback of KPCA is that the storage required for the kernel matrix grows quadratically, and the computational cost for eigenvector of the kernel matrix grows linearly with the number of training samples. Therefore, GKPCA, which can approximate KPCA with small representation error, is introduced to enhance computational efficiency. Based on the statistical learning theory and the empirical risk minimization principle, SVM has advantages of better classification accuracy and generalization performance. The extracted fault features are then used as the inputs of OAASVM to solve fault diagnosis problem. The effectiveness of the proposed approach is verified by the experimental results.

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.

METHOD OF PARAMETER STATISTICAL ANALYSIS FOR BIPOLAR INTEGRATED CIRCUIT DESIGN

The method and procedure of realizing parameter statistical correlation analysis ofbipolar analog IC’s are given,and the statistical model of parameter are constructed with doubleparameters(B_F,R_S).Based on the comparison and analysis of the circuit characteristics,it isshown that the method can be used for analysis and design of bipolar IC’s.

Hierarchical Symbolic Analysis of Large Analog Circuits with Totally Coded Method

Symbolic analysis has many applications in the design of analog circuits. Existing approaches rely on two forms of symbolic-expression representation: expanded sum-of-product form and arbitrarily nested form. Expanded form suffers the problem that the number of product terms grows exponentially with the size of a circuit. Nested form is neither canonical nor amenable to symbolic manipulation. In this paper, we present a new approach to exact and canonical symbolic analysis by exploiting the sparsity and sharing of product terms. This algorithm, called totally coded method (TCM), consists of representing the symbolic determinant of a circuit matrix by code series and performing symbolic analysis by code manipulation. We describe an efficient code-ordering heuristic and prove that it is optimum for ladder-structured circuits. For practical analog circuits, TCM not only covers all advantages of the algorithm via determinant decision diagrams (DDD) but is more simple and efficient than DDD method.

Nano-scale Bias-scalable CMOS Analog Computational Circuits Using Margin Propagation

Approximation techniques are useful for implementing pattern recognizers, communication decoders and sensory processing algorithms where computational precision is not critical to achieve the desired system level performance. In our previous work, we had proposed margin propagation (MP) as an efficient piece-wise linear (PWL) approximation technique to a log-sum-exp function and had demonstrated its advantages for implementing probabilistic decoders. In this paper, we present a systematic and a generalized approach for synthesizing analog piecewise-linear (PWL) computing circuits using the MP principle. MP circuits use only addition, subtraction and threshold operations and hence can be implemented using universal conservation principles like the Kirchoff's current law. Thus, unlike the conventional translinear CMOS current-mode circuits, the operation of the MP circuits are functionally similar in weak, moderate and strong inversion regimes of the MOS transistor making the design approach bias-scalable. This paper presents measured results from MP circuits prototyped in a 0.5μm standard CMOS process verifying the bias-scalable property. As an example, we apply the synthesis approach towards designing linear classifiers and verify its performance using measured results.

Method for Analog Circuit Soft-Fault Diagnosis and Parameter Identification Based on Indictor of Phase Deviation and Spectral Radius

The soft fault induced by parameter variation is one of the most challenging problems in the domain of fault diagnosis for analog circuits.A new fault location and parameter prediction approach for soft-faults diagnosis in analog circuits is presented in this paper.The proposed method extracts the original signals from the output terminals of the circuits under test（CUT） by a data acquisition board.Firstly,the phase deviation value between fault-free and faulty conditions is obtained by fitting the sampling sequence with a sine curve.Secondly,the sampling sequence is organized into a square matrix and the spectral radius of this matrix is obtained.Thirdly,the smallest error of the spectral radius and the corresponding component value are obtained through comparing the spectral radius and phase deviation value with the trend curves of them,respectively,which are calculated from the simulation data.Finally,the fault location is completed by using the smallest error,and the corresponding component value is the parameter identification result.Both simulated and experimental results show the effectiveness of the proposed approach.It is particularly suitable for the fault location and parameter identification for analog integrated circuits.

基于PCA和SVM的模拟电路故障诊断

针对电路故障诊断中存在的样本要求高、推广能力弱、特征提取难等问题,提出了一种基于主成分分析(PCA)与支持向量机(SVM)相结合的模拟电路故障诊断方法。通过对电路输出响应的采样信号进行PCA处理,提取故障特征的主要成分,然后利用多类SVM对各种状态下的故障模式进行分类决策,实现被测电路的故障诊断。实验结果表明:该方法能够实现电路故障的快速检测与故障元件的准确定位,具有速度快、精度高、鲁棒性好的特点。

基于PCA和PNN的模拟电路故障诊断

为了解决模拟电路故障识别困难的问题,提出一种基于主成分分析和概率神经网络的模拟电路故障诊断方法。该方法对采集到的模拟电路故障信息进行特征提取,将提取的故障特征归一化处理后输入概率神经网络,进行训练和故障模式的分类识别。实验结果表明,该方法是有效的,具有较高的故障诊断率。

基于PCA和ELM的模拟电路故障诊断

针对模拟电路的故障诊断和健康管理(PHM)的应用,提出了结合主成分分析(PCA)和极限学习机(ELM)的故障诊断方法。该方法用Sallen-Key带通滤波器来获取故障样本,并通过PCA进行故障特征提取。根据故障样本对ELM进行训练来获得故障诊断模型。实验结果表明,该实现方法识别率高、鲁棒性好,在工程实际中具有研究和应用价值。

基于PCA-LVQ的模拟电路故障诊断

为了解决模拟电路故障难于识别的问题,提出一种基于主成分分析(PCA)和学习矢量量化神经网络(LVQ)的模拟电路故障诊断新方法。该方法用PCA提取模拟电路故障特征,然后将降维后的故障特征信息输入LVQ网络训练和故障模式的分类识别。通过对Sallen-Key带通滤波器电路的故障诊断实例表明,该方法是有效的,具有较高的故障诊断率。

MRA-PCA-PSO组合优化BP神经网络模拟电路故障诊断研究

为解决模拟电路故障诊断中的特征提取困难、输入维数过高和故障信号无法进行有效分类等问题,提出一种组合优化反向传播神经网络故障诊断方法。首先采用多分辨率分析提取故障信号特征,用其能量谱构造特征向量,通过主成分分析进行降维,以减少计算规模,然后利用粒子群算法优化神经网络,克服其易陷入局部极小值的缺陷,从而达到准确识别故障类型的目的。最后以四运放高通滤波电路为测试对象,通过仿真验证该方法在模拟电路故障诊断的有效性和可靠性,结果表明能提高约10%的诊断准确率。

基于FrFT-FD和KPCA模拟电路故障特征提取方法

为获得有效的故障特征信息,提出一种基于分数阶傅里叶变换分形维的模拟电路故障特征提取方法。首先,把原始数据空间中的特征数据映射到不同的分数阶空间,分别计算不同分数阶次下故障响应信号的分形维数;然后,采用核主元分析进一步对候选特征实施降维;最后,将优化后的特征向量作为故障特征,利用神经网络进行分类诊断。仿真结果表明,本方法能很好地获取不同故障响应信号的细微差异,增强不同故障模式的可分性,提高故障诊断准确率。

基于模块化技术的最佳ULG.2的QCA电路设计

为了减少纳米器件量子细胞自动机(QCA)电路的线交叉数和电路综合时采用的门电路类型,在介绍QCA细胞结构、逻辑器件、模块化设计技术以及最佳通用逻辑门ULG.2的基础上,提出基于模块化技术的最佳通用逻辑门ULG.2的QCA电路实现方案.利用最佳QCA通用逻辑门ULG.2设计了全加/减器、全比较器和4选1数据选择器.所设计的QCA电路均用QCADesigner软件进行模拟,结果表明:该电路不仅具有正确的逻辑功能,而且某些性能得到了很大的改善.特别地对于4选1数据选择器,与已有的多数门和反相器直接设计的电路相比,细胞数、QCA线交叉数分别减少了31.8%和62.5%.

基于FRFT-KPCA的模拟电路非线性故障特征提取

针对模拟电路受非线性及元件容差影响而导致响应信号在时域和频域都出现耦合,造成故障特征提取困难的问题,结合分数阶傅里叶变换和核主成分分析理论提出一种非线性故障特征提取方法。利用分数阶傅里叶变换对耦合信号进行预处理,采用粒子群优化算法寻找最优分数阶p,实现耦合信号在分数阶域最大程度的分离。采用核主成分分析对非线性特征进行维数压缩,实现故障特征提取。实验结果表明,在时域或频域相互耦合的信号经分数阶傅里叶变换后,在分数阶域上耦合程度明显减弱,核主成分分析能够有效处理信号中的非线性信息,特征提取效果要优于其他线性特征提取方法。经过分数阶傅里叶变换和核主成分分析相结合的方法所提取的故障特征使故障模式具有更好的可分性。

基于KPCA和类峰值特征的模拟电路诊断方法

针对传统的核主成分分析方法(KPCA)无法解决在故障样本交叠严重时多分类性能较差的问题,提出一种基于改进KPCA的特征提取和类峰值特征辅助识别分类相结合的模拟电路故障诊断方法。在预处理阶段,提出了一种图像混合欧氏距离用于建立核函数,进行核主成分分析特征提取,克服了传统KPCA的局限性;并且设计了一种用类峰值特征识别的方法进行辅助识别预分类,提高分类速度。标准电路的故障诊断仿真和结果分析表明,该方法较好地克服了交叠样本给分类带来的困难,具有很好的故障识别速度和正确率。