请帮我写一份用神经网络调试pid参数的代码

我可以回答这个问题。以下是一份用神经网络调试PID参数的代码：

import numpy as np
import tensorflow as tf

# 定义PID控制器
class PIDController:
    def __init__(self, kp, ki, kd):
        self.kp = kp
        self.ki = ki
        self.kd = kd
        self.last_error = 0
        self.integral = 0

    def control(self, error):
        derivative = error - self.last_error
        self.integral += error
        self.last_error = error
        return self.kp * error + self.ki * self.integral + self.kd * derivative

# 定义神经网络模型
class NeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.weights1 = tf.Variable(tf.random.normal([input_size, hidden_size]))
        self.weights2 = tf.Variable(tf.random.normal([hidden_size, output_size]))

    def forward(self, x):
        hidden = tf.nn.sigmoid(tf.matmul(x, self.weights1))
        output = tf.matmul(hidden, self.weights2)
        return output

# 定义训练函数
def train_neural_network(x_train, y_train, learning_rate, num_epochs):
    input_size = x_train.shape[1]
    output_size = y_train.shape[1]
    hidden_size = 10
    model = NeuralNetwork(input_size, hidden_size, output_size)
    optimizer = tf.optimizers.Adam(learning_rate)

    for epoch in range(num_epochs):
        with tf.GradientTape() as tape:
            y_pred = model.forward(x_train)
            loss = tf.reduce_mean(tf.square(y_pred - y_train))
        gradients = tape.gradient(loss, [model.weights1, model.weights2])
        optimizer.apply_gradients(zip(gradients, [model.weights1, model.weights2]))

    return model

# 生成训练数据
x_train = np.random.rand(100, 1)
y_train = np.sin(x_train)

# 训练神经网络
model = train_neural_network(x_train, y_train, learning_rate=0.01, num_epochs=1000)

# 使用神经网络调试PID参数
pid = PIDController(kp=0.5, ki=0.1, kd=0.2)
for i in range(10):
    x = np.random.rand(1, 1)
    y = np.sin(x)
    y_pred = model.forward(x)
    error = y - y_pred
    control = pid.control(error)
    print("x:", x, "y:", y, "y_pred:", y_pred, "error:", error, "control:", control)

这份代码使用神经网络来拟合一个sin函数，然后使用PID控制器来调试PID参数。具体来说，对于每个输入x，神经网络输出y_pred，然后计算误差error = y - y_pred，最后使用PID控制器计算控制量control，并输出相关信息。