Paper Info
Title
Exploiting a three-objective evolutionary algorithm for generating Mamdani fuzzy rule-based systems
Abstract
In this paper, we propose a three-objective evolutionary algorithm to generate a set of Mamdani fuzzy rule-based systems (MFRBSs) with different tradeoffs between accuracy, rule base (RB) complexity and partition integrity. The RB, the linguistic partition granularities and the membership function (MF) parameters are concurrently learnt during the evolutionary process. In particular, the granularity learning is performed by exploiting the concept of virtual RB and an appropriate mapping strategy, and the MF parameter tuning is achieved by a piecewise linear transformation. The RB complexity is measured as the total number of conditions in the antecedents of the rules and the partition integrity is evaluated by using a purposely-defined index, based on the piecewise linear transformation. We use a chromosome composed of three parts, which codify, respectively, the RB, and, for each variable, the number of fuzzy sets and the parameters of the piecewise linear transformation of the membership functions. Results on two real-world regression problems are shown and discussed.
Year
DOI
Venue
2010
10.1109/FUZZY.2010.5583965
Fuzzy Systems
Keywords
Field
DocType
computational complexity,evolutionary computation,fuzzy set theory,fuzzy systems,knowledge based systems,regression analysis,Mamdani fuzzy rule based systems,chromosome,fuzzy sets,linguistic partition granularities,membership function parameters,regression problems,rule base complexity,three objective evolutionary algorithm,virtual RB
Function approximation,Evolutionary algorithm,Computer science,Evolutionary computation,Fuzzy set,Artificial intelligence,Fuzzy control system,Membership function,Machine learning,Fuzzy rule,Computational complexity theory
Conference
ISSN
ISBN
Citations 
1098-7584
978-1-4244-6919-2
2
PageRank 
References 
Authors
0.36
13
4
Name
Order
Citations
PageRank
Michela Antonelli127315.38
Pietro Ducange256627.63
Beatrice Lazzerini371545.56
Francesco Marcelloni4140491.43