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.
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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 BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_9
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DOI: https://doi.org/10.1007/978-3-319-34099-9_9
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