Universal Approximation of a Class of Interval Type-2 Fuzzy Neural Networks in Nonlinear Identification
Neural networks (NNs), type-1 fuzzy logic systems (T1FLSs), and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to be universal approximators, which means that they can approximate any nonlinear continuous function. Recent research shows that embedding an IT2FLS on an NN can be very ef...
Saved in:
Main Authors: | Oscar Castillo, Juan R. Castro, Patricia Melin, Antonio Rodriguez-Diaz |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2013-01-01
|
Series: | Advances in Fuzzy Systems |
Online Access: | http://dx.doi.org/10.1155/2013/136214 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Novel Method of the Generalized Interval-Valued Fuzzy Rough Approximation Operators
by: Tianyu Xue, et al.
Published: (2014-01-01) -
A Hybrid Approach for Modular Neural Network Design Using Intercriteria Analysis and Intuitionistic Fuzzy Logic
by: Sotir Sotirov, et al.
Published: (2018-01-01) -
Direct Adaptive Tracking Control for a Class of Pure-Feedback Stochastic Nonlinear Systems Based on Fuzzy-Approximation
by: Huanqing Wang, et al.
Published: (2014-01-01) -
Approximation of the semi-infinite interval
by: A. McD. Mercer
Published: (1980-01-01) -
Numerical Scheme for Finding Roots of Interval-Valued Fuzzy Nonlinear Equation with Application in Optimization
by: Ahmed Elmoasry, et al.
Published: (2021-01-01)