Title
Design Of Interval Type-2 Fuzzy Logic Systems Using Prior Knowledge Via Optimization Algorithms
Abstract
The paper presents the methods of integrating prior knowledge with a first-order Single-Input Single-Output (SISO) Interval Type-2 Takagi-Sugeno-Kang (TSK) Fuzzy Logic System (IT2FLS) for function approximation under noisy circumstances. Firstly, sufficient conditions on the antecedent and the consequent parameters of the IT2FLS are given to ensure that three kinds of prior knowledge - monotonicity, symmetry and special points, can be embedded into the IT2FLS. And then, we use three optimization algorithms - constrained least squares algorithm, active-set algorithm and hybrid learning algorithm to design the IT2FLS, respectively. The effectiveness of the three algorithms and the comparisons of their performance are demonstrated by simulation examples.
Year
DOI
Venue
2011
10.1109/FUZZY.2011.6007364
IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)
Keywords
Field
DocType
interval type-2 fuzzy logic system, optimization algorithm, prior knowledge
Function approximation,Active set method,Computer science,Control theory,Minification,Optimization algorithm,Artificial intelligence,Fuzzy control system,Monotonic function,Mathematical optimization,Algorithm design,Fuzzy logic,Machine learning
Conference
Volume
Issue
ISSN
null
null
1098-7584
Citations 
PageRank 
References 
0
0.34
7
Authors
2
Name
Order
Citations
PageRank
Tiechao Wang1566.80
Jian-Qiang Yi269589.71