A comparative analysis of reduction quality for probabilistic and possibilistic measurement models

In this article, several known and new methods of solving the measurement data interpretation problem for probabilistic and possibilistic measurement models are compared and the dependency of their quality on the completeness and accuracy of the measurement model is analyzed. It is shown that optimal use of a researcher's prior information about the measurement model allows one to significantly increase the accuracy of the interpretation of measurements. In some cases the error of possibilistic interpretation was less than that of probabilistic one, even though possibilistic interpretation minimizes the necessity of the error, rather than the mean squared error. This is due to the fact that prior information may be sufficient to model the input signal using a fuzzy vector, but insufficient to model it using a random vector.