Zhou Jingyu, Tian Shulin, Yang Chenglin, Ren Xuelong
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Comput Intell Neurosci. 2014;2014:740838. doi: 10.1155/2014/740838. Epub 2014 Dec 29.
This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments.
本文提出了一种基于极限学习机（ELM）的新型测试生成算法，该算法对于被测模拟设备（DUT）具有成本效益且风险较低。此方法使用从测试生成算法导出的测试模式来激励DUT，然后对DUT的输出响应进行采样以进行故障分类和检测。本文提出的基于ELM的新型测试生成算法主要包含三个创新点。首先，该算法通过使用ELM对响应空间进行分类来有效节省时间。其次，在脉冲响应样本数量减少的情况下，该算法能够有效避免测试精度降低。第三，提出了一种测试信号发生器的新流程以及测试生成算法中的一种测试结构，二者都非常简单。最后，上述改进和功能在实验中得到了验证。
