Autonomous learning for fuzzy systems: a review

Abstract As one of the three pillars in computational intelligence, fuzzy systems are a powerful mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy take form linguistic IF-THEN rules that easy to understand human. In this sense, inference mechanisms have been developed mimic human reasoning and decision-making. From data analytic perspective, provide an effective solution build precise predictive models from imprecise great transparency interpretability, thus facilitating wide range real-world applications. This paper presents systematic review modern methods autonomously learning data, emphasis on structure parameter schemes mainstream evolving, evolutionary, reinforcement learning-based systems. The main purpose is introduce underlying concepts, underpinning methodologies, as well outstanding performances state-of-the-art methods. It serves one-stop guide readers representative methodologies foundations or who desire apply fuzzy-based autonomous other scientific disciplines applied fields.