An adaptive learning algorithm for a neo fuzzy neuron

In the paper, a new optimal learning algorithm for a neo-fuzzy neuron (NFN) is proposed. The algorithm is characteristic in that it provides online tuning of not only the synaptic weights, but also the membership functions parameters. The proposed algorithm has both the tracking and filtering properties, so the NFN can be effectively used for prediction, filtering, and restoration of non-stationary noisy stochastic and chaotic signals. A special feature of the proposed algorithm is its computational simplicity in comparison with the other learning procedures for neuro-fuzzy systems.

• Type: Conference
• Year: 2003
• Where: EUSFLAT
• Authors: Yevgeniy Bodyanskiy, Illya Kokshenev, Vitaliy Kolodyazhniy
• Keywords: Algorithm, EUSFLAT 2003, EUSFLAT 2007, Membership Functions Parameters, Optimal Learning Algorithm