A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base ?
In this contribution, we propose a new method to automatically learn the Knowledge Base of a Fuzzy Rule-Based System by finding an appropriate Data Base using a Genetic Algorithm and considering a simple generation method to derive the Rule Base. Our genetic process learns all the components of the Data Base (number of labels, working ranges and membership function shapes for each linguistic variable) using a non-linear scaling function to adapt the fuzzy partition contexts.
learning granularity fuzzy rule-based systems data base genetic algorithms scaling functions context adaptation