Advances in Civil Engineering / 2014 / Article / Tab 1 / Research Article
Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand) Table 1 Details and structures of WANN model.
Structure Model Learning rule Transfer function Momentum Maximum epochs Runs Processing elements Hidden layers D1 MLP momentum Tan Axon 0.7 1000 3 50 1 D2 MLP momentum Tan Axon 0.7 1000 3 50 1 D3 MLP momentum Tan Axon 0.8 1000 3 50 1 D4 MLP momentum Tan Axon 0.6 1000 3 50 1 D5 MLP momentum Tan Axon 0.7 1000 3 50 1 D6 MLP momentum Tan Axon 0.7 1000 3 50 1 D7 MLP momentum Tan Axon 0.8 1000 3 50 1 D8 MLP momentum Tan Axon 0.7 1000 3 50 1 D9 MLP momentum Tan Axon 0.7 1000 3 50 1 D10 MLP momentum Tan Axon 0.9 1000 3 50 1 D11 MLP momentum Tan Axon 0.7 1000 3 50 1 D12 MLP momentum Tan Axon 0.7 1000 3 50 1 D13 MLP momentum Tan Axon 0.7 1000 3 50 1 D14 MLP momentum Tan Axon 0.6 1000 3 50 1 D15 MLP momentum Tan Axon 0.7 1000 3 50 1 D16 MLP momentum Tan Axon 0.7 1000 3 50 1 D17 MLP momentum Tan Axon 0.7 1000 3 50 1 D18 MLP momentum Tan Axon 0.7 1000 3 50 1