FUZZY NEURAL-LOGIC NETWORKS

This article describes a realization of fuzzy logic by a neural network. Each node in the network represents a premise or a conclusion. Let x be a member of the universal set, and let A be a node in the network. The value of activation of node A is taken to be the value of the membership function at point x, m_A (x). A logical operation is defined by a set of weights which are independent of x.

Given any value of x, a preproceesor will determine the values of the membership function for all the premises that correspond to the input nodes. These are treated as input to the network.

A propagation algorithm is used to emulate the inference process. When the network stabilizes, the value of activation at an output node represents the value of the membership function that indicates the degree that the given conclusion is true.

Weight assignment for the standard logical operations will be discussed. It will also be shown that the scheme allows us to define more general logical operations.