The application discloses a two-viewpoint collaborative TSK
fuzzy classification method based on residual dynamic guidance, which comprises the following steps: obtaining two characteristic
viewpoints for the same class of samples; constructing a two-viewpoint deep stack TSK
fuzzy classification model; introducing a cross-viewpoint staggered
semantic consistency constraint mechanism to
train two sub-models in the TSK
fuzzy classification model; calculating the output results of each current layer of the two
viewpoints to construct a classification residual
signal; activating the distribution stability by using a fuzzy information entropy evaluation rule, and constructing a dynamic residual weight by combining the classification residual; constructing a cross-viewpoint projection operator to map the weighted residual guidance information of one viewpoint to the original
characteristic space of the other viewpoint, and completing the dynamic mutual guidance and feature updating of the two
viewpoints layer by layer. The application retains the inherent explainability of the zero-order TSK model, is lightweight in structure design, and effectively enhances the cross-viewpoint
collaboration ability and generalization performance.