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Implementation of model-based intelligent next-generation test generator using neural networks

机译：基于神经网络的基于模型的智能下一代测试生成器的实现

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Abstract: This work, investigated the use of Neural Network technology to simulate faults and to generate input/output patterns used to diagnose electronic circuits via pattern classification. There are several types of circuits (i.e., digital, analog, hybrid (digit-analog), RF, and microwave). This study focused on digital circuits while maintaining the posture of considering other types in the future with similar solutions. The main focus was to investigate a methodology to model complex digital components using system identification neural network architectures. Using those components in software, a digital circuit was assembled. Faults indicating stuck at 1 or 0 was propagated through the circuit (one at a time). Input and output sequences were combined for each situations modeled and those sequences were classified to the known modeled behavior using the Adaptive Resonance Theory neural network algorithm. In addition, a data reduction methodology was established to generate input patterns required for the recognition scheme.!5
机译：摘要：这项工作研究了使用神经网络技术来模拟故障并生成用于通过模式分类来诊断电子电路的输入/输出模式。有几种类型的电路（即，数字，模拟，混合（数模），RF和微波）。这项研究集中在数字电路上，同时通过类似的解决方案在将来保持考虑其他类型的态势。主要重点是研究使用系统识别神经网络体系结构对复杂的数字组件建模的方法。使用软件中的这些组件，组装了数字电路。指示卡在1或0的故障通过电路传播（一次传播一次）。对于每种情况建模，将输入和输出序列组合在一起，并使用自适应共振理论神经网络算法将这些序列分类为已知的建模行为。此外，还建立了一种数据缩减方法来生成识别方案所需的输入模式。5

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