Neural network algorithms for tuning of fuzzy certainty factor expert systems

This paper presents a description of an algorithm that is used for tuning fuzzy certainty factors in a fuzzy expert system. Many expert system building tools have been developed, but only a few can produce systems that deal with inexact knowledge. One of the models that can deal with inexact knowledge is the fuzzy certainty factor expert system. Given a fuzzy certainty factor expert system, one of the very difficult tasks in its development is the tuning of the fuzzy certainty factors. This paper presents a method for using backpropagation, a well-known neural network training algorithm, for tuning of certainty factors. The fuzzy certainty factor expert system is defined, and then it is tuned by translating or mapping the fuzzy logic system to a feedforward neural network framework. The tuning then takes place as a session analogous to neural network training. This method is shown to be much more efficient than previous tuning methods. The application area of dental diagnostics is used to demonstrate the method. The system is reviewed and results that show its efficacy are discussed.