NEUROCOMPUTATIONAL APPROACH TO SOLVE A CONVEXLY COMBINED FUZZY RELATIONAL EQUATION WITH GENERALIZED CONNECTIVES

Abstract

In this paper, we present a method to solve a convexly combined fuzzy relational equation with generalized connectives. For this, we propose a neural network whose structure represents the fuzzy relational equation. Then we derive a learning algorithm by using the concept of back-propagation learning. Since the proposed method can be used for a general form of fuzzy relational equations, such fuzzy max-min or min-max relational equations can be treated as its special cases.