Variable differently implicational algorithm of fuzzy inference

Yiming Tang, Fuji Ren

Abstract: To reveal the inherent essence of current differently implicational algorithms for fuzzy inference, the variable differently implicational algorithm is put forward and investigated. The differently implicational principles are improved from variable and generalized viewpoint. Furthermore, focusing on the FMP (fuzzy modus ponens) problem, unified forms of the new algorithm are obtained for R-implications and S-implications. Following that, the optimal differently implicational solutions are achieved for several specific R-implications and S-implications. Lastly, the new algorithm makes the current differently implicational algorithms compose a united whole.