Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent human understandable knowledge. They have been applied to various applications and areas throughout the soft computing literature. However, FRBSs suffers from many drawbacks such as uncertainty representation, high number of rules, interpretability loss, high computational time for learning etc. To overcome these issues with FRBSs, there exists many extensions of FRBSs. This paper presents an overview and literature review of recent trends on various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), hierarchical fuzzy system (HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which use cluster centroids as fuzzy rules. The review is for years 2010-2021. This paper also highlights important contributions, publication statistics and current trends in the field. The paper also addresses several open research areas which need further attention from the FRBSs research community.

翻译：模糊规则系统（FRBSs）是一种基于规则的体系，通过使用语言模糊变量作为前件和后件来表示人类可理解的知识。在软计算文献中，它们已被广泛应用于各类场景和领域。然而，FRBSs存在诸多缺陷，例如不确定性表征、规则数量庞大、可解释性降低、学习计算耗时过长等。为克服这些问题，学术界提出了多种FRBSs扩展方法。本文对近年（2010-2021年）模糊系统（FRBSs）中各类主要类型与重要领域的研究趋势进行了概述与文献综述，涵盖遗传模糊系统（GFS）、分层模糊系统（HFS）、神经模糊系统（NFS）、演化模糊系统（eFS）、面向大数据的FRBSs、面向不平衡数据的FRBSs、FRBSs的可解释性以及采用聚类质心作为模糊规则的FRBSs。本文同时重点阐述了该领域的重要贡献、发表统计数据和当前研究趋势，并指出了若干需要FRBSs研究界进一步关注的开放式研究方向。