An unsupervised learning method for comparing the quality of the soft computing algorithms in analog systems diagnostics

摘要

The paper presents the method of assessing the difficulty of the analog system for the diagnostics using soft computing algorithms. As the latter exploit knowledge from data sets obtained from simulations of the diagnosed systems, the method estimates the diagnostic difficulty of the system based on the data set analysis. This allows comparison of various systems and diagnostic methods. The versatile method of the data sets' difficulty based on the graph clustering algorithm is proposed and explained. It is applied to test fuzzy logic and rough sets against the sixth order Butterworth lowpass filter. Conclusions and future prospects supplement the paper.