Title: A human
interface to a Neuro-Fuzzy-Perceptron
Abstract: We have developed a Neuro-Fuzzy-Perceptron which combines
the capability of neuronal networks to learn with the rule system of a Fuzzy
Controller.
Expert knowledge and company strategies for analyses can easily be modeled
which -despite their complexity- always remain transparent. This article
focuses on the human interface which enables the expert to build complex
models. We have learned that fuzzy logic only will be accepted as a controlling
tool, if an easy to use graphical interface is provided for the decision
makers. In most cases, gaining expert knowledge is a try and error process,
in which the expert reflects his rule base for the first time. While trying
out some little changes in his initial settings, he gets a feeling of the
influences of the changes. An abstraction of his knowledge is built in steps.
It is important to have an interface in which the expert intuitively finds
the spots where to he has to change the initial setting to satisfy his wishes.
Authors: D. Schweigert, H. Ohnsorge, and Oliver Sauerzapf