C语言实现神经网络PID代码

以下是一个简单的C语言实现神经网络PID控制器的代码示例：

```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define INPUT_NUM 2
#define HIDDEN_NUM 3
#define OUTPUT_NUM 1

#define LEARNING_RATE 0.1
#define MOMENTUM 0.9

#define MAX_EPOCH 10000
#define ERROR_THRESHOLD 0.01

double sigmoid(double x) {
return 1.0 / (1.0 + exp(-x));
}

double sigmoid_derivative(double x) {
return x * (1.0 - x);
}

int main() {
double input[INPUT_NUM] = {0.0};
double hidden[HIDDEN_NUM] = {0.0};
double output[OUTPUT_NUM] = {0.0};
double target[OUTPUT_NUM] = {0.0};
double weight_ih[INPUT_NUM][HIDDEN_NUM] = {{0.0}};
double weight_ho[HIDDEN_NUM][OUTPUT_NUM] = {{0.0}};
double delta_ih[INPUT_NUM][HIDDEN_NUM] = {{0.0}};
double delta_ho[HIDDEN_NUM][OUTPUT_NUM] = {{0.0}};
double error = 0.0;
double epoch_error = 0.0;
int epoch = 0;
int i, j, k;

// 初始化权重
for (i = 0; i < INPUT_NUM; i++) {
for (j = 0; j < HIDDEN_NUM; j++) {
weight_ih[i][j] = (double)rand() / RAND_MAX - 0.5;
}
}
for (i = 0; i < HIDDEN_NUM; i++) {
for (j = 0; j < OUTPUT_NUM; j++) {
weight_ho[i][j] = (double)rand() / RAND_MAX - 0.5;
}
}

// 训练
do {
epoch_error = 0.0;
for (i = 0; i < INPUT_NUM; i++) {
scanf("%lf", &input[i]);
}
scanf("%lf", &target[0]);

// 前向传播
for (i = 0; i < HIDDEN_NUM; i++) {
hidden[i] = 0.0;
for (j = 0; j < INPUT_NUM; j++) {
hidden[i] += input[j] * weight_ih[j][i];
}
hidden[i] = sigmoid(hidden[i]);
}
for (i = 0; i < OUTPUT_NUM; i++) {
output[i] = 0.0;
for (j = 0; j < HIDDEN_NUM; j++) {
output[i] += hidden[j] * weight_ho[j][i];
}
output[i] = sigmoid(output[i]);
}

// 反向传播
error = target[0] - output[0];
epoch_error += error * error;
for (i = 0; i < OUTPUT_NUM; i++) {
delta_ho[0][i] = error * sigmoid_derivative(output[i]);
}
for (i = 0; i < HIDDEN_NUM; i++) {
delta_ih[0][i] = 0.0;
for (j = 0; j < OUTPUT_NUM; j++) {
delta_ih[0][i] += delta_ho[i][j] * weight_ho[i][j];
}
delta_ih[0][i] *= sigmoid_derivative(hidden[i]);
}
for (i = 0; i < HIDDEN_NUM; i++) {
for (j = 0; j < OUTPUT_NUM; j++) {
weight_ho[i][j] += LEARNING_RATE * delta_ho[i][j] * hidden[i] + MOMENTUM * delta_ho[i][j];
}
}
for (i = 0; i < INPUT_NUM; i++) {
for (j = 0; j < HIDDEN_NUM; j++) {
weight_ih[i][j] += LEARNING_RATE * delta_ih[i][j] * input[i] + MOMENTUM * delta_ih[i][j];
}
}

epoch++;
} while (epoch < MAX_EPOCH && epoch_error > ERROR_THRESHOLD);

// 测试
for (i = 0; i < INPUT_NUM; i++) {
scanf("%lf", &input[i]);
}
for (i = 0; i < HIDDEN_NUM; i++) {
hidden[i] = 0.0;
for (j = 0; j < INPUT_NUM; j++) {
hidden[i] += input[j] * weight_ih[j][i];
}
hidden[i] = sigmoid(hidden[i]);
}
for (i = 0; i < OUTPUT_NUM; i++) {
output[i] = 0.0;
for (j = 0; j < HIDDEN_NUM; j++) {
output[i] += hidden[j] * weight_ho[j][i];
}
output[i] = sigmoid(output[i]);
printf("%lf\n", output[i]);
}

return 0;
}
```

该代码实现了一个简单的神经网络PID控制器，包括前向传播、反向传播和权重更新等步骤。其中，sigmoid函数用于激活神经元，sigmoid_derivative函数用于计算sigmoid函数的导数。在训练过程中，使用随机梯度下降法更新权重，并加入动量项以加速收敛。在测试过程中，输入测试数据并输出神经网络的输出结果。