An Evolving Neuro-Fuzzy System with Online Learning/Self-learning

An Evolving Neuro-Fuzzy System with Online Learning/Self-learning

Автор: Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Anastasiia O. Deineko

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 2 vol.7, 2015 года.

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A new neuro-fuzzy system's architecture and a learning method that adjusts its weights as well as automatically determines a number of neurons, centers' location of membership functions and the receptive field's parameters in an online mode with high processing speed is proposed in this paper. The basic idea of this approach is to tune both synaptic weights and membership functions with the help of the supervised learning and self-learning paradigms. The approach to solving the problem has to do with evolving online neuro-fuzzy systems that can process data under uncertainty conditions. The results proves the effectiveness of the developed architecture and the learning procedure.

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Computational intelligence, evolving neuro-fuzzy system, online learning/ self-learning, membership function, prediction/forecasting, machine learning

Короткий адрес: https://sciup.org/15014726

IDR: 15014726

Список литературы An Evolving Neuro-Fuzzy System with Online Learning/Self-learning

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