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arXiv 1908.101222019-08-27cs.AIcs.NE

Heuristic design of fuzzy inference systems: A review of three decades of research

ETH Zürich（苏黎世联邦理工学院）；University of Reading（雷丁大学）；University of Pretoria（比勒陀利亚大学）；Machine Intelligence Research Labs (MIR Labs)（机器智能研究实验室（MIR实验室））；Technical University of Ostrava（俄斯特拉发工业大学）

Varun Ojha, Ajith Abraham, Vaclav Snasel

备注：: 53 pages, 16 figures
DOI：: 10.1016/j.engappai.2019.08.010

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This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi-Sugeno-Kang-type fuzzy systems. Whereas, the NFS combines the FIS with neural network learning systems to improve the approximation ability. An HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. An EFS solves the data streaming issues by evolving the system incrementally, and an MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. This paper offers a synthesis of these dimensions and explores their potentials, challenges, and opportunities in FIS research. This review also examines the complex relations among these dimensions and the possibilities of combining one or more computational frameworks adding another dimension: deep fuzzy systems.

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