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Table 3 Weights and biases of the hidden and output layers used in the developed ANN model for the prediction of CGlc and CPhe

From: Prediction of phenolic compounds and glucose content from dilute inorganic acid pretreatment of lignocellulosic biomass using artificial neural network modeling

Node, j Weights and biases of the hidden layer Weights and biases of the output layer
IWj,1 IWj,2 IWj,3 IWj,4 IWj,5 IWj,6 b1,j LW1,j LW2,j b2,k
1 0.4211 0.5806 0.1236 0.2909 0.1691 0.5445 − 0.0095 0.0448 0.4378 − 0.0381
2 0.3931 0.1473 0.2595 0.4280 0.2980 0.4272 − 0.0393 0.3972 0.4253 − 0.0051
3 0.3056 0.1740 0.3713 0.1522 0.1229 0.1367 − 0.0287 0.0356 0.2299  
4 0.5184 0.0507 0.2491 0.0508 0.3027 0.4435 − 0.0379 0.6842 0.1607  
5 0.3112 0.4807 0.7085 0.2731 0.3305 0.6146 0.0394 0.2159 0.0281  
6 0.1854 0.5232 0.0776 0.4782 0.3439 0.1783 − 0.0306 0.1249 0.4419  
7 0.0370 0.2334 0.0454 0.3650 0.2354 0.4857 0.0372 0.3608 0.0639  
8 0.2985 0.2512 0.3173 0.1907 0.1834 0.5006 0.0361 0.5057 0.4299  
9 0.3070 0.4097 0.0005 0.5172 0.3769 0.2365 − 0.0184 0.0140 0.3469  
10 0.3136 0.2505 0.1438 0.0805 0.0552 0.1178 − 0.0320 0.2159 0.6152  
11 0.6273 0.0770 0.3091 0.3422 0.3044 0.3722 0.0394 0.6076 0.3147  
12 0.5133 0.5409 0.0001 0.4964 0.2515 0.1423 0.0371 0.5611 0.2426  
  1. j is the neuron node in the hidden layer (from j = 1 to 12). IWj,i (i = 1, 2, 3, 4, 5, and 6) is the neural net weight to jth neuron of the hidden layer from ith input variable; and LWk,j (k = 1 and 2) is the neural net weight to kth output variable from jth neuron of the hidden layer. b1,j is the bias of input variables, and b2,k is the bias of output layer