stm32单片机实现BP神经网络控制的代码
时间: 2024-03-29 14:15:35 浏览: 268
可以在单片机上计算的BP神经网络C语言工程(修改bug版)
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下面是一个简单的基于 STM32 单片机的 BP 神经网络控制代码示例:
```c
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "stm32f10x.h"
#define INPUT_NODES 2
#define HIDDEN_NODES 3
#define OUTPUT_NODES 1
#define LEARNING_RATE 0.5
#define EPOCHS 50000
float inputs[INPUT_NODES];
float hiddenWeights[INPUT_NODES][HIDDEN_NODES];
float hiddenBiases[HIDDEN_NODES];
float hiddenOutputs[HIDDEN_NODES];
float outputWeights[HIDDEN_NODES][OUTPUT_NODES];
float outputBiases[OUTPUT_NODES];
float outputOutputs[OUTPUT_NODES];
float targets[OUTPUT_NODES];
void initialize() {
// 初始化输入层和隐藏层的权重和偏置
for (int i = 0; i < INPUT_NODES; i++) {
for (int j = 0; j < HIDDEN_NODES; j++) {
hiddenWeights[i][j] = (float)rand() / RAND_MAX * 2 - 1;
}
}
for (int i = 0; i < HIDDEN_NODES; i++) {
hiddenBiases[i] = (float)rand() / RAND_MAX * 2 - 1;
}
// 初始化隐藏层和输出层的权重和偏置
for (int i = 0; i < HIDDEN_NODES; i++) {
for (int j = 0; j < OUTPUT_NODES; j++) {
outputWeights[i][j] = (float)rand() / RAND_MAX * 2 - 1;
}
}
for (int i = 0; i < OUTPUT_NODES; i++) {
outputBiases[i] = (float)rand() / RAND_MAX * 2 - 1;
}
}
float sigmoid(float x) {
return 1.0 / (1.0 + expf(-x));
}
float sigmoidDerivative(float x) {
return sigmoid(x) * (1.0 - sigmoid(x));
}
void feedforward() {
// 计算隐藏层的输出
for (int i = 0; i < HIDDEN_NODES; i++) {
float sum = 0;
for (int j = 0; j < INPUT_NODES; j++) {
sum += inputs[j] * hiddenWeights[j][i];
}
sum += hiddenBiases[i];
hiddenOutputs[i] = sigmoid(sum);
}
// 计算输出层的输出
for (int i = 0; i < OUTPUT_NODES; i++) {
float sum = 0;
for (int j = 0; j < HIDDEN_NODES; j++) {
sum += hiddenOutputs[j] * outputWeights[j][i];
}
sum += outputBiases[i];
outputOutputs[i] = sigmoid(sum);
}
}
void backpropagate() {
// 计算输出层的误差
for (int i = 0; i < OUTPUT_NODES; i++) {
float error = targets[i] - outputOutputs[i];
float derivative = sigmoidDerivative(outputOutputs[i]);
float delta = error * derivative;
// 更新输出层权重和偏置
for (int j = 0; j < HIDDEN_NODES; j++) {
float hiddenOutput = hiddenOutputs[j];
outputWeights[j][i] += hiddenOutput * delta * LEARNING_RATE;
}
outputBiases[i] += delta * LEARNING_RATE;
}
// 计算隐藏层的误差
for (int i = 0; i < HIDDEN_NODES; i++) {
float error = 0;
for (int j = 0; j < OUTPUT_NODES; j++) {
float delta = targets[j] - outputOutputs[j];
float derivative = sigmoidDerivative(outputOutputs[j]);
error += delta * derivative * outputWeights[i][j];
}
float derivative = sigmoidDerivative(hiddenOutputs[i]);
float delta = error * derivative;
// 更新隐藏层权重和偏置
for (int j = 0; j < INPUT_NODES; j++) {
float input = inputs[j];
hiddenWeights[j][i] += input * delta * LEARNING_RATE;
}
hiddenBiases[i] += delta * LEARNING_RATE;
}
}
void train() {
for (int epoch = 0; epoch < EPOCHS; epoch++) {
// 生成随机输入
inputs[0] = (float)rand() / RAND_MAX * 2 - 1;
inputs[1] = (float)rand() / RAND_MAX * 2 - 1;
// 计算目标输出
float x = inputs[0];
float y = inputs[1];
targets[0] = sinf(x) * cosf(y);
// 前向传递和反向传播
feedforward();
backpropagate();
}
}
int main(void) {
// 初始化
initialize();
// 训练
train();
// 测试
inputs[0] = 0.5;
inputs[1] = -0.5;
feedforward();
printf("Output: %f\n", outputOutputs[0]);
while (1) {
}
}
```
该代码实现了一个简单的 BP 神经网络,其中包含 2 个输入节点、3 个隐藏节点和 1 个输出节点,用于学习在给定输入值的情况下预测目标输出。在训练过程中,随机生成输入和目标输出,并使用反向传播算法更新神经网络的权重和偏置。在训练完成后,可以使用训练好的神经网络进行预测。
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