celinag.txt

BP神经网络,可以进行任意发酵过程建模,包括初始化,训练与测试,

0, 6.63, 100, 6.63, 100, 100, 0, 4.89, 0, 0, 5, 0
4, 6.63, 99, 6.63, 100, 100, 0.05, 4.18, 0, 0.0136, 4.993, 0
8, 6.63, 81.7, 6.63, 99, 100, 0.183, 4.163, 0, 0.050, 4.863, 0
12, 6.63, 64.3, 6.63, 81.7, 99, 0.317, 4.147, 0, 0.098, 4.672, 0
16, 6.63, 47, 6.63, 64.3, 81.7, 0.45, 4.13, 0, 0.588, 4.425, 0
20, 6.69, 5, 6.63, 47, 64.3, 1.16, 4.03, 0, 0.901, 4.334, 0
24, 6.19, 2, 6.69, 5, 47, 1.29, 3.85, 0, 0.946, 4.237, 0
28, 5.31, 0, 6.19, 2, 5, 1.46, 3.83, 0, 0.9856, 4.185, 0
32, 4.957, 0, 5.31, 0, 2, 1.303, 3.83, 0, 1.010, 4.135, 0
36, 4.25, 0, 4.957, 0, 0, 0.99, 3.83, 0, 1.035, 4.083, 0
40, 4.31, 5, 4.25, 0, 0, 0.75, 3.8, 0.18, 1.059, 4.033, 0
44, 4.31, 2, 4.31, 5, 0, 0.31, 3.78, 0.36, 1.104, 3.975, 0
48, 4.38, 0, 4.31, 2, 5, 1.25, 3.75, 0.31, 1.143, 3.917, 0
52, 4.44, 0, 4.38, 0, 2, 1.74, 3.7, 0.26, 1.168, 3.860, 0
56, 4.47, 0, 4.44, 0, 0, 1.78, 3.69, 0.24, 1.192, 3.802, 0
60, 4.52, 0, 4.47, 0, 0, 1.93, 3.67, 0.21, 1.217, 3.743, 0
64, 4.56, 0, 4.52, 0, 0, 2.08, 3.65, 0.18, 1.242, 3.683, 0
68, 4.69, 3, 4.56, 0, 0, 2.34, 3.58, 0.18, 1.265, 3.624, 0
72, 4.81, 2, 4.69, 3, 0, 2.6, 3.5, 0.15, 1.296, 3.560, 0
0, 6.2, 100, 6.2, 100, 100, 0, 4.92, 0.23, 0, 5, 0
4, 6.19, 100, 6.2, 100, 100, 0, 4.89, 0.35, 0, 5, 0
8, 6.19, 97, 6.19, 100, 100, 0, 4.82, 0.65, 0.129, 4.9333, 0
12, 6.13, 97, 6.19, 97, 100, 0, 4.75, 0.82, 0.140, 4.900, 0
16, 6.22, 87, 6.13, 97, 97, 0.17, 4.64, 0.865, 0.651, 4.645, 0
20, 6.31, 77, 6.22, 87, 97, 0.34, 4.53, 0.91, 1.269, 4.291, 0
24, 5.13, 65, 6.31, 77, 87, 1.25, 4.15, 0.91, 2.131, 3.8059, 0.0987
28, 3, 79, 5.13, 65, 77, 1.89, 4.1, 0.93, 2.163, 3.752, 0.263
32, 2.69, 82, 3, 79, 65, 2.06, 4, 0.94, 2.177, 3.706, 0.429
36, 2.63, 87, 2.69, 82, 79, 2.15, 4, 0.96, 2.186, 3.673, 0.595
40, 2.595, 86, 2.63, 87, 82, 2.195, 4, 0.985, 2.2022, 3.635, 0.664
44, 2.56, 85, 2.595, 86, 87, 2.24, 4, 1.01, 2.216, 3.595, 0.732
48, 2.5, 81, 2.56, 85, 86, 1.81, 3.85, 1.03, 2.242, 3.538, 0.798
52, 2.5, 76, 2.5, 81, 85, 2.26, 3.8, 1.1, 2.277, 3.465, 0.864
56, 2.31, 55, 2.5, 76, 81, 1.92, 3.75, 1.16, 2.277, 3.302, 0.924
60, 2.27, 39.67, 2.31, 55, 76, 1.947, 3.47, 1.213, 2.277, 3.118, 0.982
64, 2.23, 24.33, 2.27, 39.67, 55, 1.974, 3.18, 1.267, 2.277, 2.903, 1.037
68, 2.19, 9, 2.23, 24.33, 39.67, 2, 2.9, 1.32, 2.277, 2.761, 1.088
72, 2.13, 7, 2.19, 9, 24.33, 2.48, 2.85, 1.48, 2.277, 2.6455, 1.138
0, 6.7, 100, 6.7, 100, 100, 0.77, 4.89, 0, 0, 5, 0
4, 6.665, 100, 6.7, 100, 100, 0.77, 4.57, 0, 0, 5, 0
8, 6.63, 100, 6.665, 100, 100, 0.77, 4.25, 0, 0, 5, 0
12, 6.63, 96, 6.63, 100, 100, 0.88, 3.9, 0.34, 0.174, 4.0910, 0
16, 6.63, 85, 6.63, 96, 100, 1.16, 3.85, 0.45, 0.755, 4.622, 0
20, 6.56, 6.9, 6.63, 85, 96, 2.6, 3.87, 0.47, 1.809, 4.118, 0
24, 6.19, 64, 6.56, 69, 85, 2.55, 3.7, 0.53, 2.252, 3.704, 0
28, 4.69, 75.5, 6.19, 64, 69, 2.625, 3.66, 0.61, 2.343, 3.581, 0.169
32, 3.19, 87, 4.69, 75.5, 64, 2.7, 3.62, 0.69, 2.368, 3.548, 0.344
36, 3, 88, 3.19, 87, 75.5, 2.79, 3.55, 0.75, 2.386, 3.519, 0.520
40, 3, 82, 3, 88, 87, 3.17, 3.45, 0.75, 2.529, 3.459, 0.608
44, 3, 77, 3, 82, 88, 3.09, 3.3, 0.84, 2.666, 3.390, 0.7001
48, 3, 64, 3, 77, 82, 2.95, 3.25, 0.89, 3.034, 3.270, 0.795
52, 3, 59.5, 3, 64, 77, 3.03, 3.19, 0.815, 3.207, 3.161, 0.901
56, 3, 55, 3, 59.5, 64, 3.11, 3.13, 0.74, 3.393, 3.043, 1.010
60, 3, 47, 3, 55, 59.5, 3.64, 2.65, 0.65, 3.698, 2.901, 1.122
64, 3, 40, 3, 47, 55, 3.81, 2.1, 0.65, 3.995, 2.747, 1.240
68, 3, 5, 3, 40, 47, 4.61, 1.95, 0.64, 4.298, 2.551, 1.362
72, 3.09, 4, 3, 5, 40, 3.52, 1.95, 0.63, 4.337, 2.430, 1.362
0, 6.63, 100, 6.63, 100, 100, 0.34, 4.94, 0, 0, 5, 0
4, 6.563, 95.5, 6.63100, 100, 100, 0.49, 4.835, 0.12, 0.198, 4.899, 0
8, 6.5, 91, 6.563, 95.5, 100, 0.64, 4.73, 0.24, 0.427, 4.750, 0
12, 6.63, 87, 6.5, 91, 95.5, 0.6, 4.31, 0.57, 0.661, 4.566, 0
16, 6.38, 84, 6.63, 87, 91, 1.48, 4.05, 0.59, 0.866, 4.365, 0
20, 5.25, 86, 6.38, 84, 87, 1.65, 3.95, 0.85, 0.920, 4.220, 0.0714
24, 4, 75, 5.25, 86, 84, 2.59, 3.7, 0.9, 1.606, 3.851, 0.143
28, 4.03, 52, 4, 75, 86, 4.065, 3.565, 0.97, 3.382, 3.482, 0.262
32, 4.06, 29, 4.03, 52, 75, 5.54, 3.43, 1.04, 5.576, 3.016, 0.491
36, 4, 16, 4.06, 29, 52, 4.78, 2.85, 1.17, 7.057, 2.563, 0.831
40, 4.06, 12, 4, 16, 29, 7.18, 2.55, 1.54, 7.725, 2.158, 1.193
44, 4, 6, 4.06, 12, 16, 8.62, 1.85, 1.83, 8.596, 1.886, 1.573
48, 4.06, 17, 4, 6, 12, 8.5, 1.83, 1.94, 8.161, 1.554, 1.919
52, 4.06, 16, 4.06, 17, 6, 8.92, 1.39, 2.125, 8.493, 1.231, 2.193
56, 4.06, 15, 4.06, 16, 17, 9.33, 0.95, 2.31, 8.800, 0.920, 2.417
60, 4, 15, 4.06, 15, 16, 8.17, 0.68, 2.45, 9.00, 0.628, 2.594
64, 4, 28, 4, 15, 15, 7.96, 0.4, 2.9, 8.305, 0.393, 2.712
68, 4, 21, 4, 28, 15, 7.65, 0.2, 2.95, 8.517, 0.123, 2.748
72, 4, 20, 4, 21, 28, 7.42, 0.05, 2.94, 8.517, 0, 2.748
0, 6.69, 100, 6.69, 100, 100, 0, 4.9, 0, 0, 5, 0
4, 6.75, 100, 6.69, 100, 100, 0.04, 4.81, 0.11, 0, 5, 0
8, 6.75, 100, 6.75, 100, 100, 1.27, 4.25, 0.28, 0, 5, 0
12, 6.75, 98, 6.75, 100, 100, 1.51, 4.08, 0.31, 0.084, 4.955, 0
16, 6.73, 88.3, 6.75, 98, 100, 1.447, 3.87, 0.323, 0.568, 4.718, 0
20, 6.71, 78.7, 6.73, 88.3, 98, 1.383, 3.66, 0.337, 1.147, 4.382, 0
24, 6.69, 69, 6.71, 78.7, 88.3, 1.32, 3.45, 0.35, 1.830, 3.958, 0
28, 5.25, 66, 6.69, 69, 78.7, 1.49, 3.3, 0.43, 2.138, 3.765, 0.141
32, 5.1875, 65, 5.25, 66, 69, 1.6225, 3.175, 0.505, 2.330, 3.582, 0.302
36, 5.125, 64, 5.1875, 65, 66, 1.755, 3.05, 0.58, 2.525, 3.398, 0.472
40, 5.0625, 63, 5.125, 64, 65, 1.8875, 2.925, 0.655, 2.723, 3.212, 0.558
44, 5, 62, 5.0625, 63, 64, 2.02, 2.8, 0.73, 2.923, 3.0266, 0.650
48, 5, 69, 5, 62, 63, 2.98, 2.6, 1.09, 3.036, 2.882, 0.747
52, 5, 51, 5, 69, 62, 4.23, 2.45, 1.29, 4.420, 2.573, 0.840
56, 5, 33, 5, 51, 69, 3.95, 2.3, 1.52, 6.043, 2.197, 0.961
60, 5, 24, 5, 33, 51, 5.82, 1.85, 1.54, 7.021, 1.841, 1.107
64, 5, 14.5, 5, 24, 33, 7.53, 1.55, 1.575, 8.071, 1.463, 1.250
68, 5, 5, 5, 14.5, 24, 8.24, 1.25, 1.61, 9.134, 1.158, 1.262
72, 5, 9, 5, 5, 14.5, 8.9, 1, 1.42, 9.184, 0.897, 1.136