**7. Conclusions**

In this paper, the GGS of PAS, MAS, and PAMS was modeled and analyzed, which gives out a new idea to enhance the existing methods involving fuzzy membership to deal with MADM problems with partial attribute values and weights unknown. A fuzzy attributes expansion method was proposed. The proposed method can be applied to some research fields, such as regression, clustering, and fuzzy evaluation, which is proven to be effective in four examples when only part KFAs were given. By application of this method, the results of FCE were more consistent with the actual situation than traditional FCE.

For this method, it was necessary and critical to find out the appropriate size of the UFA number sequence for different practical problems by experiments. We are now considering applying this method to evaluate the power system under massive attack, where partial attribute information of some nodes could be incomplete or even totally unknown for researchers. Meanwhile, extending the proposed method from fuzzy set theory to other related fuzzy theories is also worth considering.

**Author Contributions:** Conceptualization, J.Z.; methodology, J.Z and W.S.; software, J.Z.; validation, J.Z., W.S., and Y.L.; formal analysis, J.Z.; investigation, J.Z.; resources, J.Z.; data curation, J.Z.; writing—original draft preparation, J.Z.; writing—review and editing, J.Z.; visualization, J.Z.; supervision, W.S. and Y.L.; project administration, W.S.; funding acquisition, Y.L.

**Funding:** This research was funded by the National Natural Science Foundation of China (grant number 61503240) and the Shanghai Maritime University Graduate Student Innovation Fund Project (grant number 2016ycx078).

**Acknowledgments:** The authors would like to thank classmates and teachers in the Shanghai Maritime University—Schneider Electric Joint Laboratory. The authors would like to thank all the reviewers for their valuable comments.

**Conflicts of Interest:** The authors declare no conflict of interest.
