Методы определения приоритета входных параметров функционально-математических библиотек с закрытым исходным кодом

Abstract

This work mainly aimed to identify the most suitable calculation approaches and methods for the effect of inputs upon the outputs of a functionally-mathematical library with inaccessible source code, and further use these methods and approaches to develop an input ranking tool. These methods and approaches were studied using a generalized regressive model representing an arbitrary functionally-mathematical library (an executable module). The paper studied two approaches to the determination of input effects upon the outputs: 1) correlation analysis; 2) neural network method. The first method is analytical calculation (couple by couple) of Pierson correlation coefficients for all input and output parameters. As an alternative, the study also investigated rank-based Spierman and Kendall correlations. The second approach basically meant neural network learning with required accuracy using the variation set of executable module inputs and their respective outputs with subsequent calculation of contribution from each parameter to the overall result for the neural network after learning. These approaches have also been compared in terms of several major criteria, i.e. application peculiarities, speed and output quality. The analysis has shown that neural-network learning method, despite its certain drawbacks, is more suitable for the task of this study. The paper also outlined possible ways for further improvement of this method and complexity increase of the suggested functionally-mathematical library model.