Paper Info
Title
Heuristic design of fuzzy inference systems: A review of three decades of research.
Abstract
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 ofers 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.
Year
DOI
Venue
2019
10.1016/j.engappai.2019.08.010
Engineering Applications of Artificial Intelligence
Keywords
Field
DocType
Evolutionary algorithms,Genetic fuzzy systems,Neuro-fuzzy systems,Hierarchical fuzzy systems,Evolving fuzzy systems,Multi-objective fuzzy systems,Deep fuzzy system
Interpretability,Heuristic,Evolutionary algorithm,Computer science,Fuzzy logic,Curse of dimensionality,Artificial intelligence,Fuzzy control system,Maximization,Machine learning,Genetic fuzzy systems
Journal
Volume
ISSN
Citations 
85
0952-1976
3
PageRank 
References 
Authors
0.37
0
3
Name
Order
Citations
PageRank
Varun Kumar Ojha1329.25
Ajith Abraham28954729.23
Václav Snasel31261210.53