Adaptive Wavelet-neuro-fuzzy Network in the Forecasting and Emulation Tasks
نویسندگان
چکیده
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 network architecture and its learning algorithm justifies the effectiveness of proposed approach.
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