模拟数字电路故障诊断新方法

利用小波变换与神经网络相结合的方法,采用能量分布特征提取方法和改进BP算法,给出了一种基于小波变换和BP神经网络相结合的模拟电路故障诊断方法.用正弦信号仿真模拟电路,应用小波变换对模拟电路的采样信号进行多尺度分解,再进行能量分布特征提取,然后利用神经网络对各种状态下的特征向量进行分类识别,实现模拟电路故障诊断.在用神经网络诊断模拟电路的基础上,进行了将神经网络用于数字电路单故障诊断的研究.对两者的实例电路仿真结果表明,神经网络可以有效、方便地实现电路的故障检测和定位,准确率高,为故障诊断的研究提供了一种新思路.     

一种基于神经网络的组合电路测试生成算法

介绍了一种基于神经网络的组合电路测试生成算法。利用Hopfield神经网络模型将组合电路表示成对应的神经网络,通过建立被测电路的约束网络,构造神经网络的能量函数,使组合电路的测试矢量对应神经网络能量函数的最小值点,使得测试生成问题数学化,并使用遗传算法求解能量函数的最小值点得到故障电路的测试矢量。通过在一些标准电路的实验表明,该测试生成算法有效可行。     

基于神经网络信息融合技术的模拟电路故障诊断

研究了神经网络信息融合技术在模拟电路故障诊断中的应用。利用BP神经网络建立信息融合中心,对多传感器数据进行融合处理,减少模拟电路故障诊断的不确定性。对标准电路的输出电压与电源电流特征信息,及两者融合信息的故障诊断性能比较,表明神经网络信息融合方法用于模拟电路故障诊断是有效和可行的。     

神经网络在电路故障诊断方面的应用

神经网络是一种具有优越的联想、推测、记忆功能,并且反应速度较快的网络技术,它能够通过调整电路内部大量节点之间相互连接的关系,达到诊断故障的目的,因此受到越来越多的人的关注,现已成为故障诊断的一种有效方法和手段。本文介绍了神经网络的相关知识,并且通过对于神经网络在电路故障诊断方面的具体应用,证明了神经网络在电路故障诊断方面的可行性与精确性。     

基于神经网络模型测试生成的学习策略

本文描述一种基于组合电路的Ｈｏｐｆｉｅｌｄ神经网络模型的测试生成系统，重点介绍了系统中实现神经网络学习并记忆基于电路拓扑的知识信息的学习策略，从而将基于电路拓扑的知识与数学计算结合起来，最后给出了实验结果。     

粒子群算法优化特征和神经网络的模拟电路故障诊断

模拟电路受到自身特性和外界环境的影响,故障变化具有非线性、时变性,针对当前模拟电路故障诊断模型的特征和分类器参数不匹配的难题,提出一种粒子群算法选择特征和神经网络的模拟电路故障诊断模型。首先对当前模拟电路故障诊断现状进行分析,指出它们存在的缺陷;然后提取模拟电路故障诊断特征,利用神经网络作为模拟电路故障诊断分类器;最后采用粒子群算法对模拟电路故障特征与神经网络参数进行优化,在Matlab 2012平台进行了仿真实验。结果表明,该模型的模拟电路故障诊断性能要远远优于其他参比模型,具有广泛的应用前景。     

一种基于小波-神经网络的模拟电路故障诊断方法

小波分析具有数据压缩和特征提取的特性,神经网络具有非线性映射和学习推理的优点。结合两者的特点,提出了一种基于小波与神经网络的模拟电路故障诊断方法,该方法用小波变换对电路响应信号进行特征提取,从而简化神经网络的结构,降低计算的复杂度,加快了训练速度。对实例仿真表明,该法能有效地对模拟电路进行故障诊断。     

遗传小波神经网络的非线性模拟电路故障诊断方法

提出了一种将遗传算法(GA)、神经网络与小波变换相结合对非线性模拟电路进行故障诊断的方法;分析了传统BP型神经网络在非线性模拟电路故障诊断中存在的缺陷;提出了一种新的解决方法--利用小波变换对非线性电路故障信号进行预处理,对故障信号中的冗余信息进行剔除,然后利用遗传算法优化BP网络参数,如网络权值、阈值等.利用该方法对非线性电路进行故障诊断,有利于提高神经网络对电路故障诊断的智能性及识别故障类别的能力,提高故障诊断的精度与速度.实验结果表明,该方法是可行的.     

模拟神经元电路实现研究现状与进展

人工神经网络是现代信息处理领域的一个重要的方法。相对于软件实现 ,硬件实现方式能充分发挥神经网络并行处理的特点。用模拟电路实现神经网络电路形式简单、功耗低、速度快、占用芯片面积小 ,可以提高在神经网络芯片上神经元的集成度 ,神经元电路适合用模拟电路实现。文中综述了当前神经网络单元的模拟 VLSI实现的成果、新技术以及作者的工作成果。针对应用最广泛的线性和平方突触神经元 ,详细从权值存储单元、突触电路和阈值函数电路三方面来叙述。对各种实现方式的优缺点进行了比较 ,同时指出了神经网络实现电路中需要考虑的因素。最后 ,展望了用集成电路技术实现自学习神经网络的发展方向     

一种适用于功能电路的自组织神经网络模型构建

借鉴信息在生物体神经系统内的传递过程及电子电路的构造特点,提出了一种适用于功能电路的自组织神经网络模型及构建方法.首先提出了神经元、神经网络模型,分析了信号传递过程;然后描述了功能电路到神经网络的映射关系,给出了神经网络自组织特性实现方法;最后以2×2乘法器为功能电路,验证了此模型对功能电路的承载性及自组织特性.结果表...     

GaAs IC CAD及其在通信电路中的应用技术

介绍了自主开发的多个完整实用的 Ga As IC CAD软件 ,包括微波无源元件建模、微波有源器件测试建模、微波毫米波 IC CAD、光电集成电路 CAD、 Ga As VHSIC CAD、微波高速 MCM CAD等 ,并简要介绍了针对 Ga As工艺线的建库工作。以上软件和库已用于多个通信电路的设计。     

前馈型神经网络容错性研究的现状和展望

本文首先明确具体地给出了前馈型神经网络(以下简称前馈网络)容错性的基本概念及其研究内容,进而系统地对前馈网络容错性研究的各种分析和设计方法进行了简要的介绍和评述。最后提出了前馈网络容错性有待进一步研究的若干主要问题。     

On the application and design of artificial neural networks formotor fault detection. I

The general design considerations for feedforward artificial neural networks (ANNs) to perform motor fault detection are presented. A few noninvasive fault detection techniques are discussed, including the parameter estimation approach, human expert approach, and ANN approach. A brief overview of feedforward nets and the backpropagation training algorithm, along with its pseudocodes, is given. Some of the neural network design considerations such as network performance, network implementation, size of training data set, assignment of training parameter values, and stopping criteria are discussed. A fuzzy logic approach to configuring the network structure is presented     

An extraction technique for small signal intrinsic parameters of HEMTs based on artificial neural networks

This paper presents a fast and accurate procedure for extraction of small signal intrinsic parameters of AlGaAs/GaAs high electron mobility transistors (HEMTs) using artificial neural network (ANN) techniques. The extraction procedure has been done in a wide range of frequencies and biases at various temperatures. Intrinsic parameters of HEMT are acquired using its values of common-source S-parameters. Two different ANN structures have been constructed in this work to extract the parameters, multi layer perceptron (MLP) and radial basis function (RBF) neural networks. These two kinds of ANNs are compared to each other in terms of accuracy, speed and memory usage. To validate the capability of the proposed method in small signal modeling of GaAs HEMTs, data and modeled values of S-parameters of a 200 μm gate width 0.25 μm GaAs HEMT are compared to each other and very good agreement between them is achieved up to 30 GHz. The effect of bias, temperature and frequency conditions on the extracted parameters of HEMT has been investigated, and the obtained results match the theoretical expectations. The proposed model can be inserted to computer-aided design (CAD) tools in order to have an accurate and fast design, simulation and optimization of microwave circuits including GaAs HEMTs.     

移动通信用GaAs微波单片集成电路的CAD技术研究

介绍了一个移动通信用 Ga As MMIC集成电路 CAD系统。该系统包括单片电路器件模型系统和 CAD软件包。应用该系统已完成多个移动通信电路的优化设计 ,取得良好效果。     

Measurement selection for parametric IC fault diagnosis

This article presents experimental results which show feedforward neural networks are well-suited for analog IC fault diagnosis. Boundary band data (BBD) measurement selection is used to reduce the computational overhead of the FFN training phase. We compare the diagnostic accuracy between traditional statistical classifiers and feedforward neural networks trained with various measurement selection criteria. The feedforward networks consistently perform as well as or better than the other classifiers in term of accuracy. Training using BBD consistently reduces the FFN training efforts without degrading the performance. Experimental results suggest that feedforward networks provide a cost efficient method for IC fault diagnosis in a large scale production testing environment.This work is supported by NSF-IUC CDADIC, Project 90-1.     

Theory and applications of neural networks for industrial controlsystems

The theory and the applications of artificial neural networks, especially in a control field, are described. Recurrent networks and feedforward networks are discussed. Application to pattern recognition, information processing, design, planning, diagnosis, and control are examined. Hybrid systems using the neural networks, fuzzy sets, and artificial intelligence (AI) technologies are surveyed     

Multilayer feedforward neural networks with single powers-of-twoweights

A new algorithm for designing multilayer feedforward neural networks with single powers-of-two weights is presented. By applying this algorithm, the digital hardware implementation of such networks becomes easier as a result of the elimination of multipliers. This proposed algorithm consists of two stages. First, the network is trained by using the standard backpropagation algorithm. Weights are then quantized to single powers-of-two values, and weights and slopes of activation functions are adjusted adaptively to reduce the sum of squared output errors to a specified level. Simulation results indicate that the multilayer feedforward neural networks with single powers-of-two weights obtained using the proposed algorithm have generalization performance similar to that of the original networks with continuous weights     

一种动态筛选样本的前向神经网络快速学习算法

从理论上讨论了一类隐含层激励函数满足Mercer条件的前向神经网络学习问题,分析了提高网络学习速度的途径,提出了一种动态筛选样本的前向神经网络快速学习算法。它大大提高了网络学习速度,克服了传统的基于梯度下降的网络学习方法存在的诸多弊端。算法还具有动态确定隐含层神经元数的自构性优点。文中通过具体数值试验验证了上述算法的可行性和优越性。     

A posteriori error learning in nonlinear adaptive filters

The authors provide relationships between the a priori and a posteriori errors of adaptation algorithms for real-time output-error nonlinear adaptive filters realised as feedforward or recurrent neural networks. The analysis is undertaken for a general nonlinear activation function of a neuron, and for gradient-based learning algorithms, for both a feedforward (FF) and recurrent neural network (RNN). Moreover, the analysis considers both contractive and expansive forms of the nonlinear activation functions within the networks. The relationships so obtained provide the upper and lower error bounds for general gradient based a posteriori learning in neural networks