DeepAI AI Chat
Log In Sign Up

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

by   Varun Ojha, et al.

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.


page 1

page 2

page 3

page 4


Literature Review of various Fuzzy Rule based Systems

Fuzzy rule based systems (FRBSs) is a rule-based system which uses lingu...

Recommendations on Designing Practical Interval Type-2 Fuzzy Systems

Interval type-2 (IT2) fuzzy systems have become increasingly popular in ...

Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees

This paper proposes a design of hierarchical fuzzy inference tree (HFIT)...

Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering

Time-varying classifiers, namely, evolving classifiers, play an importan...

Evolutionary Algorithms for Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) is a complex systems modeling technique whic...

An Exploratory Study of Hierarchical Fuzzy Systems Approach in Recommendation System

Recommendation system or also known as a recommender system is a tool to...