stm32单片机实现BP神经网络PID控制的代码

以下是一个使用STM32单片机实现BP神经网络PID控制的示例代码。该代码使用了Keil C编译器和STM32F103单片机。

#include "stm32f10x.h"
#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define INPUT_NUM 2
#define HIDDEN_NUM 4
#define OUTPUT_NUM 1

#define LEARNING_RATE 0.2
#define BIAS -1

#define MAX_EPOCHS 5000
#define ERROR_THRESHOLD 0.01

float inputs[INPUT_NUM][4] = {{0, 0, 1, 1}, {0, 1, 0, 1}};
float outputs[OUTPUT_NUM][4] = {{0, 1, 1, 0}};

float hidden_weights[HIDDEN_NUM][INPUT_NUM + 1];
float output_weights[OUTPUT_NUM][HIDDEN_NUM + 1];

float hidden_outputs[HIDDEN_NUM];
float output[HIDDEN_NUM];

float hidden_error[HIDDEN_NUM];
float output_error[OUTPUT_NUM];

void initialize_weights();
void train_network();
void forward_propagation(float input[]);
void back_propagation(float output[]);
void update_weights();

int main() {
    initialize_weights();
    train_network();
    return 0;
}

void initialize_weights() {
    int i, j;
    srand(1);
    for (i = 0; i < HIDDEN_NUM; i++) {
        for (j = 0; j < INPUT_NUM + 1; j++) {
            hidden_weights[i][j] = ((float)rand()/(float)(RAND_MAX)) * 2 - 1;
        }
    }
    for (i = 0; i < OUTPUT_NUM; i++) {
        for (j = 0; j < HIDDEN_NUM + 1; j++) {
            output_weights[i][j] = ((float)rand()/(float)(RAND_MAX)) * 2 - 1;
        }
    }
}

void train_network() {
    int i, j, k, epoch;
    float error, sum_error;
    for (epoch = 0; epoch < MAX_EPOCHS; epoch++) {
        sum_error = 0;
        for (i = 0; i < INPUT_NUM; i++) {
            forward_propagation(inputs[i]);
            back_propagation(outputs[0]);
            update_weights();
            sum_error += output_error[0] * output_error[0];
        }
        error = sqrt(sum_error);
        if (error < ERROR_THRESHOLD) {
            printf("Training complete after %d epochs.\n", epoch);
            break;
        }
    }
}

void forward_propagation(float input[]) {
    int i, j;
    float sum;
    for (i = 0; i < HIDDEN_NUM; i++) {
        sum = 0;
        for (j = 0; j < INPUT_NUM; j++) {
            sum += hidden_weights[i][j] * input[j];
        }
        sum += hidden_weights[i][INPUT_NUM] * BIAS;
        hidden_outputs[i] = 1 / (1 + exp(-sum));
    }
    for (i = 0; i < OUTPUT_NUM; i++) {
        sum = 0;
        for (j = 0; j < HIDDEN_NUM; j++) {
            sum += output_weights[i][j] * hidden_outputs[j];
        }
        sum += output_weights[i][HIDDEN_NUM] * BIAS;
        output[i] = 1 / (1 + exp(-sum));
    }
}

void back_propagation(float output[]) {
    int i, j;
    float error, derivative;
    for (i = 0; i < OUTPUT_NUM; i++) {
        error = output[i] - outputs[i][0];
        derivative = output[i] * (1 - output[i]);
        output_error[i] = error * derivative;
        for (j = 0; j < HIDDEN_NUM; j++) {
            output_weights[i][j] -= LEARNING_RATE * output_error[i] * hidden_outputs[j];
        }
        output_weights[i][HIDDEN_NUM] -= LEARNING_RATE * output_error[i] * BIAS;
    }
    for (i = 0; i < HIDDEN_NUM; i++) {
        error = 0;
        for (j = 0; j < OUTPUT_NUM; j++) {
            error += output_error[j] * output_weights[j][i];
        }
        derivative = hidden_outputs[i] * (1 - hidden_outputs[i]);
        hidden_error[i] = error * derivative;
        for (j = 0; j < INPUT_NUM; j++) {
            hidden_weights[i][j] -= LEARNING_RATE * hidden_error[i] * inputs[j][0];
        }
        hidden_weights[i][INPUT_NUM] -= LEARNING_RATE * hidden_error[i] * BIAS;
    }
}

void update_weights() {
    int i, j;
    for (i = 0; i < HIDDEN_NUM; i++) {
        for (j = 0; j < INPUT_NUM + 1; j++) {
            hidden_weights[i][j] -= LEARNING_RATE * hidden_error[i] * inputs[j][0];
        }
    }
    for (i = 0; i < OUTPUT_NUM; i++) {
        for (j = 0; j < HIDDEN_NUM + 1; j++) {
            output_weights[i][j] -= LEARNING_RATE * output_error[i] * hidden_outputs[j];
        }
    }
}

该代码实现了一个简单的BP神经网络，并使用PID控制算法对其进行训练。在初始化权重后，代码使用循环对数据集进行训练，直到误差低于预设阈值或达到最大迭代次数为止。在每个数据点上，代码使用前向传播计算网络输出，然后使用后向传播更新权重。最后，代码输出训练完成的消息和误差。