A decision fusion-based method for global sensitivity analysis of complicated experiments

In real world, the relationship between experimental input and output has become more and more complex, which brings great challenges to the sensitivity analysis. This paper proposes a decision fusion based global sensitivity analysis method for complicated experiments, which not only provides quantitative evluation of the input factor influence on experimental results, but also mines the correlation and form the explicit criteria in IF-THEN fomation for further guidance. The theory of decision information system and continuous attribute discretization is presented first for transforming the experimental input and output into a decision table. In order to calculate the sensitivity of the factors and extract valid correlation criterions between conditional attributes and decision attributes simultaneously, the discrimination matrx is utilized for attribute reduction. Then a sensitivity analysis method based on decision fusion is proposed by organically assembling experiment design, attribute discretization, the discrimization matrix, and attribute reduction. Finally, the effectiveness and practicality of the proposed method were verified by the application of sensitivity analysis in hypersonic vehicle re-entry trajectory experiment.