Automatic bandwidth selection of fuzzy membership functions
Information Sciences: an International Journal
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Generating fuzzy membership function with self-organizing feature map
Pattern Recognition Letters
Dynamical membership functions: an approach for adaptive fuzzy modelling
Fuzzy Sets and Systems
H∞ estimation for fuzzy membership function optimization
International Journal of Approximate Reasoning
Handling constraints in global optimization using an artificial immune system
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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A clonal selection algorithm (Clonalg) inspires from Clonal Selection Principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. It takes place in various scientific applications and it can be also used to determine the membership functions in a fuzzy system. The aim of the study is to adjust the shape of membership functions and a novice aspect of the study is to determine the membership functions. Proposed method has been implemented using a developed Clonalg program for a single input and output fuzzy system. In the previous work [1], using genetic algorithm (GA) is proposed to it. In this study they are compared, too and it has been shown that using clonal selection algorithm is advantageous than using GA for finding optimum values of fuzzy membership functions.