基于python构建NARX网络

以下是一个使用Python构建NARX网络的示例：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense, LSTM, RepeatVector, TimeDistributed
from keras.callbacks import EarlyStopping

# 加载数据
data = np.load('sunspot.npy')
train_ratio = 0.7
train_size = int(train_ratio * len(data))
train_data = data[:train_size]
test_data = data[train_size:]

# 定义NARX模型
model = Sequential()
model.add(LSTM(100, activation='relu', input_shape=(2, 1)))
model.add(RepeatVector(1))
model.add(LSTM(100, activation='relu', return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.compile(optimizer='adam', loss='mse')

# 训练模型
X_train, y_train = [], []
for i in range(2, len(train_data)):
    X_train.append([[train_data[i-2]], [train_data[i-1]]])
    y_train.append(train_data[i])
X_train, y_train = np.array(X_train), np.array(y_train)
early_stop = EarlyStopping(monitor='loss', patience=1, verbose=1)
model.fit(X_train, y_train, epochs=50, callbacks=[early_stop], verbose=1)

# 测试模型
X_test, y_test = [], []
for i in range(2, len(test_data)):
    X_test.append([[test_data[i-2]], [test_data[i-1]]])
    y_test.append(test_data[i])
X_test, y_test = np.array(X_test), np.array(y_test)
y_pred = model.predict(X_test)

# 绘制预测结果
plt.plot(y_test, label='Actual')
plt.plot(y_pred, label='Predicted')
plt.legend()
plt.xlabel('Month')
plt.ylabel('Sunspot Number')
plt.title('Sunspot Number Prediction using NARX')
plt.show()

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print('Mean Squared Error:', mse)

在这个示例中，我们使用了Keras库来构建一个NARX模型。我们使用LSTM层来处理序列数据，并使用RepeatVector和TimeDistributed层来实现NARX结构。我们使用太阳黑子数据集来训练和测试模型，并绘制了预测结果。最后，我们计算了测试集上的均方误差。