Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

The increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.     

重叠社区检测及其果蝇视觉进化神经网络

针对社区发现中,部分节点划分难的问题,探讨重叠社区检测的优化模型和求解的视觉进化神经网络.模型通过设计节点隶属度矩阵和节点分割规则,建立以模糊分割阈值为变量,且能评估社区划分效果的改进型模块度函数;算法设计中,以候选解构成的状态矩阵对应函数值矩阵作为输入,依据果蝇视觉系统的信息处理机制,建立以输出作为状态学习率的果蝇视...     

基于卷积神经网络的滚动轴承故障诊断算法

现有的滚动轴承故障诊断算法依赖于人工特征提取,当负载发生变化或故障样本数较少时,诊断效果较差。针对该问题,文中建立了基于卷积神经网络的智能故障诊断算法。首先,将原始振动数据进行分段预处理;其次,建立基于卷积神经网络的故障诊断模型实现对故障特征的自适应提取;最后,利用Softmax分类器实现对故障诊断结果的输出。将该算法应用于振动数据库实验,验证了该算法的有效性,当样本分布不平衡时,依然保持较高的识别率。     

基于神经网络与证据理论的模拟电路故障诊断

论述了利用多类电量测试信息、应用神经网络与D-S证据理论实现模拟电路故障诊断的基本原理,提出了一种基于可测点电压与不同测试频率下的电路增益经决策层信息融合的故障诊断新方法.分别利用此两类测试信息,各用一个独立的改进BP网络对电路进行初步诊断,再运用所提融合诊断算法实现故障定位.模拟实验结果表明:所提方法对硬故障与元件参数偏移较小的软故障均适用,故障定位准确率高.     

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。     

基于进化策略的BP神经网络匹配算法研究

研究了利用BP神经网络理论进行景象匹配的方法,将灰度不变矩和边缘不变矩同时作为匹配特征应用于BP神经网络景象匹配中,并对经典的进化策略进行改进,提出并实现了一种高效的基于进化策略的BP神经网络景象匹配算法.实验结果证明了算法寻优的全局性和精确性.     

Fault detection in combinational circuits using a compressed fault table

A method is developed for obtaining a highly compressed fault table for two-level combinational circuits. A set of operations is defined through which the minimal test set for detecting stuck-at faults is obtained from the compressed fault table. The method is equally suitable for sum of products form or product of sums form realization of logic functions and generates the test set directly from the algebraic expression of the logic function.     

A neural network algorithm for testing stuck-open faults in CMOS combinational circuits

In this article, an automatic test pattern generation technique using neural network models for stuck-open faults in CMOS combinational circuits is presented. For a gate level fault model of stuck-open faults in CMOS circuits, SR(slow-rise) and SF(slow-fall) gate transition faults we develop a neural network representation. A neural network computation technique for generating robust test patterns for stuck-open faults is given. The main result is extending previous efforts in stuck-at test pattern generation to stuck-open test pattern generation using neural network models. A second result is an extension of the technique to robust test pattern generation.     

Fault detection in combinational circuits using Gunn effect logic devices

It is shown that certain realisations of combinational switching functions using Gunn effect logic gates can be tested for single or multiple stuck-type faults by using two tests only. This result is achieved by exploiting the fact that the function of Gunn effect logic gates is sensitive to bias voltage     

基于径向基函数神经网络的模拟/混合电路故障诊断

径向基函数神经网络是一种前馈型神经网络,具有较强的函数逼近能力和分类能力,学习速度快等优点.本文采用幅值恒定的正弦信号源进行模拟电路的故障仿真,从频域提取输出信号波形的特征值建立故障字典,应用径向基函数神经网络的这些优点进行响应分析和故障诊断,能够实现快速故障诊断及定位,具有准确率高的特点.     

Analog electronic neural network circuits

It is argued that the large interconnectivity and the precision required in neural network models present novel opportunities for analog computing. Analog circuits for a wide variety of problems such as pattern matching, optimization, and learning have been proposed and a few have been built. Most of the circuits built so far are relatively small, exploratory designs. Circuits implementing several different neural algorithms, namely, template matching, associative memory, learning, and two-dimensional resistor networks inspired by the architecture of the retina are discussed. The most mature circuits are those for template matching, and chips performing this function are now being applied to pattern-recognition problems. Examples of analog implementation are examined     

ECG beat recognition using fuzzy hybrid neural network

This paper presents the application of the fuzzy neural network for electrocardiographic (ECG) beat recognition and classification. The new classification algorithm of the ECG beats, applying the fuzzy hybrid neural network and the features drawn from the higher order statistics has been proposed in the paper. The cumulants of the second, third, and fourth orders have been used for the feature selection. The hybrid fuzzy neural network applied in the solution consists of the fuzzy self-organizing subnetwork connected in cascade with the multilayer perceptron, working as the final classifier. The c-means and Gustafson-Kessel algorithms for the self-organization of the neural network have been applied. The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution. The investigations show that the method may find practical application in the recognition and classification of different type heart beats.     

神经网络在组合电路故障模拟测试生成算法中的应用

本文在基于故障模拟的测试生成算法基础上,提出了一种初始测试矢量的生成方法,即采用神经元网络模型来生成初始矢量,既避免了随机生成初始矢量的盲目性,又避免了确定性算法使用回溯所带来的大运算量。试验结果证明这种方法是有效的。     

A hybrid fuzzy logic and neural network algorithm for robot motioncontrol

Robotic manipulators are multivariable nonlinear coupling dynamic systems. Industrial robots were controlled by using a traditional controller, the control performance of which may change with respect to operating conditions. Since the robotic manipulators have complicated nonlinear mathematical models, control systems based on the system model are difficult to design. In this paper, a model-free hybrid fuzzy logic and neural network algorithm was proposed to control this multi-input/multi-output (MIMO) robotic system. First, a fuzzy logic controller was designed to control individual joints of this 4-degree-of-freedom (DOF) robot. Secondly, a coupling neural network controller was introduced to take care of the coupling effect among joints and refine the control performance of this robotic system. The experimental results showed that the application of this control strategy effectively improved the trajectory tracking precision     

一种NOMA系统中的反馈深度神经网络检测算法

在上行非正交多址(Non-orthogonal Multiple Access, NOMA)系统中,针对传统基于串行干扰消除(Successive Interference Cancellation, SIC)检测存在同个时频块内用户间干扰的问题,提出了一种新型的NOMA检测算法。通过将SIC检测的反馈消除结构和深度神经网络结合起来,设计出了一种新型的反馈深度神经网络(Feedback Deep Neural Network, FDNN)结构。FDNN模型分为两个模块,检测模块通过深度神经网络实现非线性检测,反馈模块通过权重矩阵重构信号并消除用户干扰。通过重复检测和反馈过程,FDNN依次检测出各个用户的符号,并达到了良好的性能。仿真结果表明FDNN检测算法相较于SIC检测具有更低的误符号率和误比特率,并验证了其具有更良好的抗用户间干扰的性能。     

基于遗传神经网络的入侵检测模型

这篇文章提出了一种基于遗传神经网络的入侵检测模型-进化神经网络入侵检测系统(ENNIDS),模型的核心模块利用遗传算法优化神经网络来实现,结合了误用检测和异常检测技术,并从理论上分析了该模型各个模块的功能和实现技术.我们在UCI机器学习数据库的入侵检测数据集上进行了实验,实验结果表明:该模型在检测正确率、误警率等方面能获得校好的性能。     

基于BP神经网络算法的入侵检测技术

在入侵检测中应用神经网络技术,可以大大提高入侵检测的检测率,有效提高网络数据的安全。本文分析了BP神经网络应用于入侵检测的实现方式及存在的问题,并对现有的BP神经网络算法进行改进,阐述了基于BP神经网络入侵检测系统及仿真实验。     

应用于非均匀性校正的改进的神经网络算法

非制冷红外焦平面的非均匀性对红外系统的图像质量造成严重影响。神经网络的自适应调节性优于传统的定标校正方法,成为研究热点。但是传统的神经网络存在期望值不准确、误差函数精度不高和学习速度不适应网络变化的缺点。本文将目标像元与其4邻近像元的像素值进行比较,按偏差值的大小进行排序,再增加权系数来计算期望值;文章又分析了神经网络出现的局部极小问题,在原有的误差函数基础上引入了隐层饱和度的计算式;并提出了根据总误差值之比来调节学习速度。经仿真实验表明,新算法较好地降低了非均匀度。     

Frequency division multiplexing in analogue neural network

Frequency division multiplexing has been studied as a means of communication between neural layers in an analogue multilayered perceptron neural network architecture, trained using the back-propagation learning algorithm. Simulation results on network learning and generalisation show that the neural network is tolerant to as much as 50% overlap of frequency responses of filters used in demultiplexing. Thus, the number of communication channels available is considerably increased.<>     

Shortest path routing algorithm using Hopfield neural network

A near-optimal routing algorithm employing a modified Hopfield neural network (HNN) is presented. Since it uses every piece of information that is available at the peripheral neurons, in addition to the highly correlated information that is available at the local neuron, faster convergence and better route optimality is achieved than with existing algorithms that employ the HNN. Furthermore, all the results are relatively independent of network topology for almost all source-destination pairs