Fuzzy Rule Learning with Linguistic Modifiers

Abstract

The use of fuzzy rule-based systems in regression problems is widely extended due to the precision of the obtained models. Moreover, the use of Mamdani models is usually referred to as a good choice in many real problems, since it provides an interpretable and precise functional relationship between the output and input variables. In this paper we present a new leaning Mamdani fuzzy system FRLC-Rgress (Fuzzy Rule Learning through Clustering for Regression Problems). This provides an accurate fuzzy system and simple Mamdani fuzzy rule bases for regression problems. FRLC-Rgress based on linguistic modifiers and fuzzy clustering achieves a low complexity of the learned models while keeping a high accuracy, by following two stages: multi- granularity, fuzzy discretization of the variables, and perceptual learning of the fuzzy rules. FRLC-Rgress is experimented using six real-world datasets. It outperforms two of the most and simple fuzzy systems (genetic fuzzy systems) in state of the art.