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Heuristic design of fuzzy inference systems: A review of three decades of research
- Source :
- Engineering Applications of Artificial Intelligence. 85:845-864
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
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 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.<br />53 pages, 16 figures
- Subjects :
- FOS: Computer and information sciences
0209 industrial biotechnology
Fuzzy inference
Computer Science - Artificial Intelligence
Computer science
Evolutionary algorithm
02 engineering and technology
Fuzzy logic
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Neural and Evolutionary Computing (cs.NE)
Electrical and Electronic Engineering
Interpretability
Heuristic
business.industry
Computer Science - Neural and Evolutionary Computing
Maximization
Fuzzy control system
Artificial Intelligence (cs.AI)
Control and Systems Engineering
Genetic fuzzy systems
020201 artificial intelligence & image processing
Artificial intelligence
business
Curse of dimensionality
Subjects
Details
- ISSN :
- 09521976
- Volume :
- 85
- Database :
- OpenAIRE
- Journal :
- Engineering Applications of Artificial Intelligence
- Accession number :
- edsair.doi.dedup.....7b2e75715f1f0a00e5c09e39047d2b8e