BP神经网络主轴承预警代码

以下是一个简单的BP神经网络主轴承预警代码示例：

import numpy as np

class BPNN:
    def __init__(self, input_size, hidden_size, output_size, learning_rate):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.learning_rate = learning_rate
        
        self.weights1 = np.random.randn(hidden_size, input_size+1)
        self.weights2 = np.random.randn(output_size, hidden_size+1)
        
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def sigmoid_derivative(self, x):
        return x * (1 - x)
    
    def forward(self, x):
        x = np.append(x, [1])
        hidden = self.sigmoid(np.dot(self.weights1, x))
        hidden = np.append(hidden, [1])
        output = self.sigmoid(np.dot(self.weights2, hidden))
        return output
    
    def train(self, x, y):
        x = np.append(x, [1])
        hidden = self.sigmoid(np.dot(self.weights1, x))
        hidden = np.append(hidden, [1])
        output = self.sigmoid(np.dot(self.weights2, hidden))
        
        output_error = y - output
        output_delta = output_error * self.sigmoid_derivative(output)
        
        hidden_error = np.dot(self.weights2.T, output_delta)
        hidden_delta = hidden_error[:-1] * self.sigmoid_derivative(hidden[:-1])
        
        self.weights2 += self.learning_rate * np.outer(output_delta, hidden)
        self.weights1 += self.learning_rate * np.outer(hidden_delta, x)

然后，我们可以使用上面的代码来训练一个BP神经网络模型来进行主轴承预警，例如：

input_data = np.array([[0.1, 0.2, 0.3],
                       [0.4, 0.5, 0.6],
                       [0.7, 0.8, 0.9]])
labels = np.array([[0.1], [0.3], [0.5]])

bpnn = BPNN(input_size=3, hidden_size=4, output_size=1, learning_rate=0.1)

for i in range(1000):
    for j in range(len(input_data)):
        bpnn.train(input_data[j], labels[j])

在训练完成之后，我们可以使用 `bpnn.forward()` 方法来进行预测，例如：

test_input = np.array([0.2, 0.3, 0.4])
prediction = bpnn.forward(test_input)
print(prediction)

这里的 `prediction` 就是模型对于输入数据 `[0.2, 0.3, 0.4]` 的预测输出。