给我一段c++编写的基于深度学习的pid控制算法

以下是一个基于深度学习的PID控制算法的C++实现示例：

#include <iostream>
#include <math.h>
#include <vector>
#include <cstdlib>
#include <ctime>

//定义神经网络结构
class NeuralNetwork {
public:
    NeuralNetwork(int num_input, int num_hidden, int num_output) {
        //初始化权重和偏置
        input_num = num_input;
        hidden_num = num_hidden;
        output_num = num_output;
        weights_ih.resize(num_hidden, std::vector<double>(num_input, 0));
        weights_ho.resize(num_output, std::vector<double>(num_hidden, 0));
        bias_h.resize(num_hidden, 0);
        bias_o.resize(num_output, 0);
        //随机初始化权重和偏置
        srand((unsigned)time(NULL));
        for (int i = 0; i < num_hidden; i++) {
            for (int j = 0; j < num_input; j++) {
                weights_ih[i][j] = (double)rand() / RAND_MAX * 2 - 1;
            }
        }
        for (int i = 0; i < num_output; i++) {
            for (int j = 0; j < num_hidden; j++) {
                weights_ho[i][j] = (double)rand() / RAND_MAX * 2 - 1;
            }
        }
        for (int i = 0; i < num_hidden; i++) {
            bias_h[i] = (double)rand() / RAND_MAX * 2 - 1;
        }
        for (int i = 0; i < num_output; i++) {
            bias_o[i] = (double)rand() / RAND_MAX * 2 - 1;
        }
    }
    //前向传播
    std::vector<double> predict(std::vector<double> input) {
        std::vector<double> hidden(hidden_num);
        std::vector<double> output(output_num);
        for (int i = 0; i < hidden_num; i++) {
            double sum = 0;
            for (int j = 0; j < input_num; j++) {
                sum += input[j] * weights_ih[i][j];
            }
            hidden[i] = sigmoid(sum + bias_h[i]);
        }
        for (int i = 0; i < output_num; i++) {
            double sum = 0;
            for (int j = 0; j < hidden_num; j++) {
                sum += hidden[j] * weights_ho[i][j];
            }
            output[i] = sigmoid(sum + bias_o[i]);
        }
        return output;
    }
    //反向传播
    void train(std::vector<double> input, std::vector<double> target, double learning_rate) {
        std::vector<double> hidden(hidden_num);
        std::vector<double> output(output_num);
        for (int i = 0; i < hidden_num; i++) {
            double sum = 0;
            for (int j = 0; j < input_num; j++) {
                sum += input[j] * weights_ih[i][j];
            }
            hidden[i] = sigmoid(sum + bias_h[i]);
        }
        for (int i = 0; i < output_num; i++) {
            double sum = 0;
            for (int j = 0; j < hidden_num; j++) {
                sum += hidden[j] * weights_ho[i][j];
            }
            output[i] = sigmoid(sum + bias_o[i]);
        }
        //计算损失函数
        std::vector<double> output_error(output_num);
        for (int i = 0; i < output_num; i++) {
            output_error[i] = target[i] - output[i];
        }
        std::vector<double> hidden_error(hidden_num);
        for (int i = 0; i < hidden_num; i++) {
            double sum = 0;
            for (int j = 0; j < output_num; j++) {
                sum += output_error[j] * weights_ho[j][i];
            }
            hidden_error[i] = sum * sigmoid_derivative(hidden[i]);
        }
        //更新权重和偏置
        for (int i = 0; i < output_num; i++) {
            for (int j = 0; j < hidden_num; j++) {
                weights_ho[i][j] += learning_rate * output_error[i] * hidden[j];
            }
            bias_o[i] += learning_rate * output_error[i];
        }
        for (int i = 0; i < hidden_num; i++) {
            for (int j = 0; j < input_num; j++) {
                weights_ih[i][j] += learning_rate * hidden_error[i] * input[j];
            }
            bias_h[i] += learning_rate * hidden_error[i];
        }
    }
private:
    int input_num;
    int hidden_num;
    int output_num;
    std::vector<std::vector<double>> weights_ih;
    std::vector<std::vector<double>> weights_ho;
    std::vector<double> bias_h;
    std::vector<double> bias_o;
    //激活函数
    double sigmoid(double x) {
        return 1 / (1 + exp(-x));
    }
    //激活函数的导数
    double sigmoid_derivative(double x) {
        return x * (1 - x);
    }
};

//定义PID控制器
class PIDController {
public:
    PIDController(double p, double i, double d, double max_output, double min_output, double dt) {
        kp = p;
        ki = i;
        kd = d;
        max_output_value = max_output;
        min_output_value = min_output;
        delta_time = dt;
        last_error = 0;
        integral_error = 0;
    }
    //计算输出
    double calculate(double setpoint, double feedback) {
        double error = setpoint - feedback;
        double derivative_error = (error - last_error) / delta_time;
        integral_error += error * delta_time;
        double output = kp * error + ki * integral_error + kd * derivative_error;
        if (output > max_output_value) {
            output = max_output_value;
        }
        else if (output < min_output_value) {
            output = min_output_value;
        }
        last_error = error;
        return output;
    }
private:
    double kp;
    double ki;
    double kd;
    double max_output_value;
    double min_output_value;
    double delta_time;
    double last_error;
    double integral_error;
};

int main() {
    NeuralNetwork nn(1, 4, 1); //创建神经网络，输入层1个节点，隐藏层4个节点，输出层1个节点
    PIDController pid(0.2, 0.1, 0.05, 1, -1, 0.1); //创建PID控制器，P=0.2，I=0.1，D=0.05，输出范围[-1,1]，采样时间0.1s
    double setpoint = 10; //设定目标值
    double feedback = 0; //初始反馈值
    for (int i = 0; i < 100; i++) {
        std::vector<double> input{ feedback }; //神经网络输入为反馈值
        std::vector<double> output = nn.predict(input); //通过神经网络得到输出
        double output_value = output[0]; //输出值即为神经网络输出的第一个元素
        double control_value = pid.calculate(setpoint, feedback); //计算PID控制值
        feedback = output_value + control_value; //反馈值为神经网络输出值加上PID控制值
        std::vector<double> target{ feedback }; //神经网络的目标值为反馈值
        nn.train(input, target, 0.1); //用反馈值训练神经网络
        std::cout << "Setpoint: " << setpoint << ", Feedback: " << feedback << ", Output: " << output_value << ", Control: " << control_value << std::endl;
    }
    return 0;
}

这个程序实现了一个基于深度学习的PID控制器。首先创建了一个具有1个输入节点、4个隐藏节点和1个输出节点的神经网络，然后用PID控制器计算控制值，将控制值和神经网络输出值相加得到反馈值，最后用反馈值训练神经网络。神经网络的输入为反馈值，输出为反馈值的预测值。PID控制器的参数可以根据实际情况进行调整。