Abstract: The architecture of adaptive wavelet-neuro-fuzzy-network and its learning algorithm for the solving of nonstationary processes forecasting and emulation tasks are proposed. The learning algorithm is optimal on rateof convergence and allows tuning both the synaptic weights and dilations and translations parameters of waveletactivation functions. The simulation of developed wavelet-neuro-fuzzy network architecture and its learningalgorithm justifies the effectiveness of proposed approach.

Keywords: wavelet, adaptive wavelet-neuro-fuzzy network, recurrent learning algorithm, forecasting, emulation.

ACM Classification Keywords: I.2.6 Learning – Connectionism and neural nets

Link:

ADAPTIVE WAVELET-NEURO-FUZZY NETWORK IN THE FORECASTING AND EMULATION TASKS

Yevgeniy Bodyanskiy, Iryna Pliss, Olena Vynokurova

http://www.foibg.com/ijita/vol15/ijita15-1-p08.pdf