共查询到20条相似文献,搜索用时 171 毫秒
1.
电路测试生成的神经网络方法研究 总被引:1,自引:1,他引:0
本文研究将人工神经网络用于组合电路测试产生的一般模型,分析影响这一方法,效率的因素,提出了用于降低被测电路对应网络规模的故障压缩,电路分块,多级蕴涵等策略,采用改进的梯度算法加建了网络能量函数极小值的搜索。介绍了基于这些策略开发的一个测试生成系统的结构。实验结果说明了提出方法的有效性。 相似文献
2.
针对组合电路内建自测试过程中的功耗和故障覆盖率等问题,提出了一种能获得较高故障覆盖率的低功耗测试矢量生成方案。该方案先借助A talanta测试矢量生成工具,针对不同的被测电路生成故障覆盖率较高的测试矢量,再利用插入单跳变测试矢量的方法以及可配置线性反馈移位寄存器生成确定性测试向量的原理,获得低功耗测试矢量。通过对组合电路集ISCAS’85的实验,证实了这种测试生成方案的有效性。 相似文献
3.
4.
基于遗传算法的自适应测试生成 总被引:6,自引:1,他引:5
文章介绍了一种基于遗传算法的自适应测试生成方法,首先讨论了用遗传算法进行测试生成时构造评价函数的一些方法,然后应用组合电路的Hopfield神经网络模型,提出了基于遗传算法的自适应测试生成算法,该方法不同于传统的方法,它不需要故障传播传播、回退等过程,实验结果表明了本算法的可行性。 相似文献
5.
能量极小化的一种启发式遗传算法 总被引:1,自引:0,他引:1
Chakradhar et.al(1988,1990)将组合电路表示为Hopfield神经网络,将测试生成问题转化为一个组合优化总理2。本文在传统遗传算法的基础上,结合电路的拓扑信息,提出了一咱用于组合电路神经网络模型能量极小化的启发式遗传算法。 相似文献
6.
基于遗传算法生成的测试矢量集的故障覆盖率要低于确定性方法.本文分析指出造成这种现象的一个可能原因在于,组合电路测试生成过程中存在高阶、长距离模式,从而导致遗传算法容易陷人局部极值或早熟收敛.为此,本文首次提出使用分布估计算法生成测试矢量.该方法使用联合概率分布捕捉电路主输人之间的关联性。从而避免了高阶、长距离模式对算法的影响,缓解了算法早熟收敛问题.针对ISCAS-85国际标准组合电路集的实验结果表明,该方法能够获得较高的故障覆盖率. 相似文献
7.
电路测试神经网络方法中求多个测试矢量 总被引:7,自引:0,他引:7
文章研究在数字电路测试的神经网络方法中求给定故障对应的多个测试矢量的方法,首先提出了一种求多个测试矢量的遗传进化方法,然后提出了一种矢量扰动方法,通过这两种者的结合使用,能获得被测电路较小的完备测订,从而提高了电路测试神经网络的方法的性能。 相似文献
8.
9.
10.
11.
数字电路的最优神经网络模型及建立方法 总被引:7,自引:0,他引:7
本文研究电路的最优神经网络模型,获得了对任意结构的多输入多输出逻辑电路,都存在一种最优神经网络能表征电路的逻辑功能,通过求解一个线性方程组可以得到这种神经网络的结构.文中也给出了多输入基本门电路的最优神经网络结构及其能量函数的一般表达式. 相似文献
12.
Kurosh Madani 《电信纪事》1993,48(11-12):537-545
The increase in integration density and in complexity of moderns integrated circuits and systems revealed the necessity to consider the testability problem at the design level of circuits. One of the most active research areas in circuits design, over the past decade, has been the implementation of neural networks as electronic VLSI chips. Especially, the implementation of artificial neural networks (ANN) as CMOS integrated circuits shows several attractive features. Recent studies point out that classification is their most successful application field, and thus large networks will be required. Unfortunately, very few papers analyse the testability of electronic implementation of artificial neural networks. A large number of artificial neural networks models deal with binary output neurones. This paper presents and discuss a global current measurement based pseudo-analogue technique for digital-output electronic neural networks testing. Two approaches have been presented and their limitations have been discussed. Simulation results and a method validation test circuit have been presented. 相似文献
13.
本文给出了一种利用神经网络计算光流场的新算法。整个计算过程分为三个阶段:神经网络模型参数的估计,轮廓边界垂直速度分量的动态测量以及光流场的计算。通过网络能量函数和运动的约束误差函数的比较对网络参数进行估计。用一个动态算法迭代运行非线性光流场计算方法以使神经网络能量函数达到最小,同时也对垂直速度分量进行动态估计。由模拟试验结果讨论了影响神经网络收敛性能的若干因素。 相似文献
14.
H.-J. Liu Z. Liu W.-L. Jiang Y.-Y. Zhou 《Signal Processing, IET》2010,4(2):137-148
To deal with the problem of emitter identification caused by the measurement uncertainty of emitter feature parameters, this study proposes a new identification algorithm based on combination of vector neural networks (CVNN), which is deduced from the backpropagation vector neural network and can realise the nonlinear mapping between the interval-value input data and the interval-value output emitter types. The key idea of CVNN is to adopt a combination of multiple multi-input/single-output neural networks to construct an identification system; each of the networks can only realise the identification function between two emitter types. Through quantitative analysis, it can be concluded that the proposed algorithm requires less computational load in the training stage. A number of simulations are presented to demonstrate the identification capability of the CVNN algorithm for emitter signals with and without additive noise. Simulation results show that the proposed algorithm not only has better identification capability, but also is relatively more insensitive to noise. 相似文献
15.
16.
A novel method for fault diagnosis of analog circuits with tolerance based on wavelet packet (WP) decomposition and probabilistic neural networks using genetic algorithm (GPNN) is proposed in this paper. The fault feature vectors are extracted after feasible domains on the basis of WP decomposition of responses of a circuit being solved. Then by fusing various uncertain factors into probabilistic operations, GPNN methods to diagnose faults are proposed whose parameters and structure obtained form genetic optimisations resulting in best detection of faults. Finally, simulations indicated that GPNN classifiers are correct 7% more than BPNN of the test data associated with our sample circuits. 相似文献
17.
18.
光刻热点检测是集成电路可制造性设计的一项重要环节。已有研究将卷积神经网络应用于光刻热点的检测,但在卷积运算的重复性、检测结果准确度等方面存在较多问题。为了解决上述问题,提出一种基于Faster R-CNN并结合在线难例挖掘和软性非极大值抑制的光刻热点检测算法。采用ICCAD 2012 Contest的版图基准作为验证载体。实验结果表明,该算法能有效提高检测的精度和效率,平均检测耗时为0.6 h/mm2,召回率为96.1%,精确率可达40.3%。 相似文献
19.
A new method for analyzing the stabilities of analog electronic neural networks ispresented.The energy functions with clear physical meaning are derived by introducing the staticequivalent circuit models,which has expanded the Tellegen Theorem for application on circuitanalysis.The method used to derive the energy functions of nets from first order differentialequations is valid for all first order continuous autonomous systems.The stability analysis ofcellular neural networks is made by the use of the stationary cocontent theorem.Some resultsare instructive for the network implementation on circuits. 相似文献
20.
A new method for analyzing the stabilities of analog electronic neural networks is presented. The energy functions with clear
physical meaning are derived by introducing the static equivalent circuit models, which has expanded the Tellegen Theorem
for application on circuit analysis. The method used to derive the energy functions of nets from first order differential
equations is valid for all first order continuous autonomous systems. The stability analysis of cellular neural networks is
made by the use of the stationary cocontent theorem. Some results are instructive for the network implementation on circuits. 相似文献