USE OF ARTIFICAL INTELLIGENCE MODELS IN ACCEPTANCE TESTS

This paper describes the application of fuzzy perсeptron in acceptance testing problems. A study on the use of various fuzzy functions in solving the problem of approximation of probability densities was carried out. Experiments were conducted on time series with preset distribution laws, evaluating approximation quality using standard deviation in the series of 29 tests. As a result of the research, using Gaussian fuzzy function, the mean value of standard deviation equal to 0.00145 determined which confirms good approximative ability of the fuzzy perсeptron. The system was tested on the acceptance test data of the gas compressor engine. In this case, approximation quality was assessed using the Kolmogorov-Smirnov agreement criterion. Analysis results confirm the high approximative ability of the fuzzy perсeptron, emphasizing its applicability in real acceptance tests.