Paper Info
Title
Multi-Objective Optimisation For Fuzzy Modelling Using Interval Type-2 Fuzzy Sets
Abstract
This paper reports on a new Mamdani data-driven fuzzy modelling approach, which makes use of interval type-2 fuzzy sets and employs a multi-objective evolutionary algorithm to optimise the structure and parameters of interval type-2 fuzzy models with respect to the predictive accuracy and the complexity of fuzzy models. In order to reduce the computational burden of the interval type-2 fuzzy modelling, a computationally efficient type-reduction technique is developed based on the center-of-sums defuzzifier. As the clustering-based method is utilised to elicit the initial fuzzy model, a new objective function is also introduced to improve the distribution of membership functions in each variable domain. The proposed modelling approach is then tested on a benchmark problem, where it is shown to be able to conduct an interpretable interval type-2 fuzzy model while maintaining a good predictive accuracy. This approach is also applied to the problem of prediction of the mechanical properties of alloy steels, and is shown to perform well.
Year
DOI
Venue
2012
10.1109/FUZZ-IEEE.2012.6251165
2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
Keywords
Field
DocType
computational modeling,evolutionary computation,tensile strength,uncertainty,fuzzy sets,predictive models,fuzzy set theory,optimization,objective function,indexes
Neuro-fuzzy,Mathematical optimization,Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy logic,Fuzzy set,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy number,Machine learning
Conference
ISSN
Citations 
PageRank 
1098-7584
2
0.38
References 
Authors
10
2
Name
Order
Citations
PageRank
Shen Wang1228.34
Mahdi Mahfouf223533.17