Authors:
Zikrija Avdagic
;
Samir Omanovic
;
Emir Buza
and
Belma Cardakovic
Affiliation:
Faculty of Electrical Engineering in Sarajevo, Bosnia and Herzegovina
Keyword(s):
Coevolutionary algorithm, Fuzzy logic, Neural network, Mamdani fuzzy system, Hybridization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Co-Evolution and Collective Behavior
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Hybrid Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
This paper is related to a research of modelling fuzzy-neural systems using the coevolutionary algorithm, and has the focus on advantages of using the coevolutionary algorithm for system structure optimization. In the context of this work, the term fuzzy-neural system defines the system that can be used as the fuzzy system with all its functionalities or as the neural network with all its functionalities. The hybridization of fuzzy logic, neural networks and coevolutionary algorithm and its architecture are presented in general, and the role of the coevolutionary algorithm in structure optimization is described in details. Results of testing with Iris Database, from UCI Machine Learning Repository are also presented. Tests performed during the research supports the conclusion that usage of the coevolutionary algorithm for the fuzzy-neural system’s structure optimization is very efficient.