A GA PARADIGM FOR LEARNING FUZZY RULES

In this paper, we describe a paradigm for learning fuzzy rules using g enetic algorithms (GA). We formulate our problem of learning as follow s: given a set of linguistic values that characterize the input and ou tput state variables of the system in consideration, derive an n-rule fuzzy control algorithm. The value n represents a specified constraint of the GA in searching for a functional ruleset. The GA learning para digm is powerful since it requires no prior knowledge about the system 's behavior in order to formulate a set of functional control rules th rough adaptive learning. We present our simulation results using the c lassical inverted pendulum control problem to demonstrate the effectiv eness of the GA learning scheme. Results have shown that the approach has great potential as a tool for the learning of fuzzy control rules, particularly in situations where the knowledge from a human expert is not easily accessible.