神经网络结合PID代码

<< setprecision(2) << y21 << " ";
cout << fixed << setprecision(2) << y22 << " ";
cout << fixed << setprecision(2) << y23 << " ";
cout << fixed << setprecision(2) << y24 <<以下是一个简单的神经网络结合PID算法的代码示例，用于控制一个物理系统的输出 " ";
cout << fixed << setprecision(2) << y25 << " ";
cout << fixed << setprecision(2) <<。

import tensorflow as tf
import numpy as np

# PID参数
Kp = 1
Ki = 0. y26 << " ";
    cout << fixed << setprecision(2) << y27 << " ";
    cout << fixed << setprecision(1
Kd = 0.01

# 输入和输出
x = tf.placeholder(tf.float32, shape=[None, 12) << y28 << " ";
    cout << fixed << setprecision(2) << y29 << " ";

    return 0;
])
y = tf.placeholder(tf.float32, shape=[None, 1])

# 神经网络模型
hidden = tf.layers.dense(x}

输出结果为：

16.00 17.33 20.66 22.99 25.33 , 10, activation=tf.nn.relu)
output = tf.layers.dense(hidden, 1)

# PID控制
error = y - output28.00 30.66 33.33 36.00 38.66 41.33 44.00 46
integral = tf.Variable(0.0, trainable=False)
derivative = tf.Variable(0.0, trainable=False)
new_integral = integral + error
new_derivative = error - derivative
control = Kp * error + Ki * new_integral + Kd * new_derivative

.66 49.33 52.00 54.66 57.33 60.00 62.66 65.# 损失函数和优化器
loss = tf.reduce_mean(tf.square(control))
optimizer = tf.train.AdamOptimizer().minimize(loss)

33 68.00 70.66 73.33 76.00 78.66 81.33 84.00# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(1000 86.66 89.33 92.00 94.66 97.33 100.00 102.66 105.33 108.00

这样就得到了数列的前36项，其中小数点后面):
# 生成训练数据
x_train = np.random.rand(100, 1)
y_train = np.sin(x保留两位。