Uncertainty Time Series' Multi-Scale Fractional-Order Association Model

This article first systematically classified the uncertainty and provided the multi-scale fractional ordered association model in accordance with the multiple uncertainty time series. From the mathematical point of view, the model used in this thesis extended the integerorder correlation measurement to the fractional-order correlation measurement; elongate the information recognition from point to line, and rolled out the nonprocess identification to the process identification from the identification point of view. Introduced the multi-scale interaction identification method through the imitation of human beings’ process identification, and achieved the accurate identification form coarse to fine. Example shows that, fractional-order association algorithm can provide much more related information comparing with the integerorder one; the import of the multi-scale interactive iteration greatly enhanced the intelligent of the model and the correlative accuracy.