Generating fuzzy rule-based systems from examples

This paper proposes a general methodology to generate fuzzy rule-based systems automatically from examples. The objective of this work is to generate fuzzy systems with good mapping ability and generalization ability as well. This methodology consists of five steps. Inductive learning is incorporated to enhance fuzzy system's generalization ability. Experiments are conducted to evaluate the system performance of generated fuzzy systems based on two sets of data in the literature.