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Test Generation Algorithm Based on SVM with compressing Sample Space Methods

Journal from gdlhub / 2016-11-17 01:26:11
Oleh : Ting Long, Jiang Shiqi, Hang Luo, Telkomnika
Dibuat : 2015-06-01, dengan 1 file

Keyword : Test Generation, SVM (Support Vector Machine), k-nearest Neighbors, Maximal Difference
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/1473

Test generation algorithm based on the SVM (support vector machine) generates test signals derived from the sample space of the output responses of the analog DUT. When the responses of the normal circuits are similar to those of the faulty circuits (i.e., the latter have only small parametric faults), the sample space is mixed and traditional algorithms have difficulty distinguishing the two groups. However, the SVM provides an effective result. The sample space contains redundant data, because successive impulse-response samples may get quite close. The redundancy will waste the needless computational load. So we propose three difference methods to compress the sample space. The compressing sample space methods are Equidistant compressional method, k-nearest neighbors method and maximal difference method. Numerical experiments prove that maximal difference method can ensure the precision of the test generation.

Deskripsi Alternatif :

Test generation algorithm based on the SVM (support vector machine) generates test signals derived from the sample space of the output responses of the analog DUT. When the responses of the normal circuits are similar to those of the faulty circuits (i.e., the latter have only small parametric faults), the sample space is mixed and traditional algorithms have difficulty distinguishing the two groups. However, the SVM provides an effective result. The sample space contains redundant data, because successive impulse-response samples may get quite close. The redundancy will waste the needless computational load. So we propose three difference methods to compress the sample space. The compressing sample space methods are Equidistant compressional method, k-nearest neighbors method and maximal difference method. Numerical experiments prove that maximal difference method can ensure the precision of the test generation.

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