A Learning Algorithm for Forecasting Adaptive Wavelet-neuro- Fuzzy Network
نویسندگان
چکیده
The architecture of forecasting adaptive wavelet-neuro-fuzzy-network and its learning algorithm for the solving of nonstationary processes forecasting tasks are proposed. The learning algorithm is optimal on rate of convergence and allows to tune both the synaptic weights and dilations and translations parameters of wavelet activation functions. The simulation of developed wavelet-neuro-fuzzy network architecture and its learning algorithm justifies the effectiveness of proposed approach.
منابع مشابه
Adaptive Wavelet-neuro-fuzzy Network in the Forecasting and Emulation Tasks
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 rate of convergence and allows tuning both the synaptic weights and dilations and translations parameters of wavelet activation functions. The simulation of developed wavelet-neuro-fuzzy ...
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