An Evolving Neuro-Fuzzy System with Online Learning/Self-learning
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 th...
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| Veröffentlicht in: | International journal of modern education and computer science 2015-02, Vol.7 (2), p.1-7 |
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| container_title | International journal of modern education and computer science |
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| creator | Bodyanskiy, Yevgeniy V Tyshchenko, Oleksii K Deineko, Anastasiia O |
| description | 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. |
| doi_str_mv | 10.5815/ijmecs.2015.02.01 |
| format | Article |
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| ispartof | International journal of modern education and computer science, 2015-02, Vol.7 (2), p.1-7 |
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| source | EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Architecture Artificial intelligence Computer architecture Computer Science Educational Technology Electronic Learning Fuzzy logic Learning Processes Mathematics Networks Online instruction Processing speed |
| title | An Evolving Neuro-Fuzzy System with Online Learning/Self-learning |
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