Abstract

Neuro-fuzzy systems are capable of tuning theirs parameters on presented data. Both global and local techniques can be used. The paper presents a hybrid memetic approach where local (gradient descent) and global (differential evolution) approach are combined to tune parameters of a neuro-fuzzy system. Application of the memetic approach results in lower error rates than either gradient descent optimisation or differential evolution alone. The results of experiments on benchmark datasets have been statistically verified.

Keywords

Neuro-fuzzy system Memetic algorithm Differential evolution 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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