Python Elman递归神经网络是否有能力使用两个或更多的输入？
我在使用neurolab python库时遇到一个问题：我试图借助Elman递归神经网络预测一些时间序列：Python Elman递归神经网络是否有能力使用两个或更多的输入？,python,time-series,artificial-intelligence,data-mining,recurrent-neural-network,Python,Time Series,Artificial Intelligence,Data Mining,Recurrent Neural Network,我在使用neurolab python库时遇到一个问题：我试图借助Elman递归神经网络预测一些时间序列： import neurolab as nl import numpy as np # Create train samples # x = np.linspace(-7, 7, 20) x = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2
import neurolab as nl
import numpy as np
# Create train samples
# x = np.linspace(-7, 7, 20)
x = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 7, 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9, 8, 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8, 8.9, 9, 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9, 10, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11, 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 12, 12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 13, 13.1, 13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 14, 14.1, 14.2, 14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15, 15.1, 15.2, 15.3, 15.4, 15.5, 15.6, 15.7, 15.8, 15.9, 16, 16.1, 16.2, 16.3, 16.4, 16.5, 16.6, 16.7, 16.8, 16.9, 17, 17.1, 17.2, 17.3, 17.4, 17.5, 17.6, 17.7, 17.8, 17.9, 18, 18.1, 18.2, 18.3, 18.4, 18.5, 18.6, 18.7, 18.8, 18.9, 19, 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7, 19.8, 19.9]
x=np.asarray(x)
y = [0.000, 0.296, 0.407, 0.488, 0.552, 0.607, 0.655, 0.697, 0.734, 0.769, 0.800, 0.829, 0.855, 0.880, 0.903, 0.925, 0.945, 0.964, 0.982, 0.998, 1.014, 1.029, 1.043, 1.057, 1.069, 1.081, 1.092, 1.103, 1.113, 1.123, 1.132, 1.141, 1.149, 1.157, 1.164, 1.171, 1.177, 1.184, 1.189, 1.195, 1.200, 1.205, 1.209, 1.214, 1.218, 1.221, 1.225, 1.228, 1.231, 1.234, 1.236, 1.238, 1.240, 1.242, 1.244, 1.245, 1.246, 1.247, 1.248, 1.249, 1.249, 1.250, 1.250, 1.250, 1.250, 1.250, 1.249, 1.248, 1.248, 1.247, 1.246, 1.245, 1.243, 1.242, 1.240, 1.239, 1.237, 1.235, 1.233, 1.231, 1.228, 1.226, 1.224, 1.221, 1.218, 1.215, 1.213, 1.210, 1.206, 1.203, 1.200, 1.197, 1.193, 1.190, 1.186, 1.182, 1.178, 1.174, 1.170, 1.166, 1.162, 1.158, 1.154, 1.149, 1.145, 1.140, 1.136, 1.131, 1.126, 1.122, 1.117, 1.112, 1.107, 1.102, 1.096, 1.091, 1.086, 1.081, 1.075, 1.070, 1.064, 1.059, 1.053, 1.047, 1.041, 1.036, 1.030, 1.024, 1.018, 1.012, 1.006, 0.999, 0.993, 0.987, 0.981, 0.974, 0.968, 0.961, 0.955, 0.948, 0.942, 0.935, 0.928, 0.922, 0.915, 0.908, 0.901, 0.894, 0.887, 0.880, 0.873, 0.866, 0.859, 0.852, 0.844, 0.837, 0.830, 0.822, 0.815, 0.807, 0.800, 0.792, 0.785, 0.777, 0.770, 0.762, 0.754, 0.747, 0.739, 0.731, 0.723, 0.715, 0.707, 0.699, 0.691, 0.683, 0.675, 0.667, 0.659, 0.651, 0.643, 0.634, 0.626, 0.618, 0.610, 0.601, 0.593, 0.584, 0.576, 0.567, 0.559, 0.550, 0.542, 0.533, 0.525, 0.516, 0.507, 0.498, 0.490, 0.481]
y=np.asarray(y)
sample = [20, 20.1, 20.2, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8, 20.9, 21, 21.1, 21.2, 21.3, 21.4]
sample=np.asarray(sample)
size = len(x)
inp = x.reshape(size,1)
tar = y.reshape(size,1)
smp = sample.reshape(len(sample),1)
#print(inp)
print(tar)
# Create network with 2 layers and random initialized
#net = nl.net.newelm([[min(x), max(y)]],[5, 1]) # neurolab.net.newff(minmax, size, transf=None)
net = nl.net.newelm([[min(x), max(y)]], [16, 1], [nl.trans.TanSig(), nl.trans.PureLin()])
# Set initialized functions and init
net.layers[0].initf = nl.init.InitRand([-0.1, 0.1], 'wb')
net.layers[1].initf = nl.init.InitRand([-0.1, 0.1], 'wb')
net.init()
# Train network
error = net.train(inp, tar, epochs=1900, show=100, goal=0.0001)
# Simulate network
out = net.sim(smp)
print(out)