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

Yevgeniy Bodyanskiy | Zendy; Oleksii K. Tyshchenko | Zendy; Anastasiia Deineko | Zendy

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

Author(s) -

Yevgeniy Bodyanskiy,

Oleksii K. Tyshchenko,

Anastasiia Deineko

Publication year - 2015

Publication title -

international journal of modern education and computer science

Language(s) - English

Resource type - Journals

eISSN - 2075-017X

pISSN - 2075-0161

DOI - 10.5815/ijmecs.2015.02.01

Subject(s) - computer science , artificial intelligence , field (mathematics) , fuzzy logic , architecture , process (computing) , machine learning , mode (computer interface) , supervised learning , neuro fuzzy , fuzzy control system , artificial neural network , human–computer interaction , mathematics , art , pure mathematics , visual arts , operating system

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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