An Interactive Fuzzy Operator Used To Interpret Connectionist Knowledge

Abstract

In the conjugate effort of building shells for fuzzy rule-based systems with a homogenous architecture based on neural networks, a difficult task is to exhibit and explain the results of neural calculus as a parallel inference process. This paper focuses on strictly fuzzy approach of neural networks, and proposes fuzzy operators in order to extract connectionist knowledge on the base of the concept of f-duality. The methodology is tested using two known benchmarks: iris problem and portfolio problem.