stm32单片机实现BP神经网络控制的代码

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：

#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。