Memetic Neuro-Fuzzy System with Differential Optimisation

Conference paper

DOI: 10.1007/978-3-319-34099-9_9

Part of the Communications in Computer and Information Science book series (CCIS, volume 613)
Cite this paper as:
Siminski K. (2016) Memetic Neuro-Fuzzy System with Differential Optimisation. In: Kozielski S., Mrozek D., Kasprowski P., Małysiak-Mrozek B., Kostrzewa D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS 2015, BDAS 2016. Communications in Computer and Information Science, vol 613. Springer, Cham

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 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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