Neural Network Algorithms for Microcontrollers: Making Control More Humanized

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Hello everyone, I am Xiaodong. Today, let’s discuss an interesting topic – implementing neural network algorithms on microcontrollers. Sounds impressive, right? Don’t worry, follow me step by step, and I guarantee you’ll easily master this “intelligent brain”.

1.

What is a Neural Network?

In simple terms, a neural network is an algorithm that mimics the way the human brain works. It connects a large number of “neurons” to transmit information layer by layer, ultimately arriving at a result. Just like when we see a cat, the brain quickly processes the image information and concludes, “This is a cat.”

Implementing a neural network on a microcontroller is essentially simulating this process with code. We input data (like sensor readings) into the network, perform a series of calculations, and finally obtain output results (like control commands).

2.

Hardware Preparation

For entry-level neural network implementation, we do not need very powerful hardware. A common 32-bit ARM microcontroller is sufficient, such as the STM32F4 series. If you have a more advanced microcontroller at hand, that’s even better.

Neural Network Algorithms for Microcontrollers: Making Control More Humanized

Don’t forget to prepare several sensors as input, as well as some actuators (like servos and motors) as output. This way, we can create a truly intelligent system that can perceive the environment and react accordingly.

3.

Code Implementation

Alright, the main event is here. Below is a simplified implementation of a neural network, written in C:

#include <math.h>

#define INPUT_NODES 3
#define HIDDEN_NODES 4
#define OUTPUT_NODES 2

float input[INPUT_NODES];
float hidden[HIDDEN_NODES];
float output[OUTPUT_NODES];
float weights_ih[INPUT_NODES][HIDDEN_NODES];
float weights_ho[HIDDEN_NODES][OUTPUT_NODES];

float sigmoid(float x) {
    return 1 / (1 + exp(-x));
}

void forward_propagation() {
    // Calculate the hidden layer
    for (int i = 0; i < HIDDEN_NODES; i++) {
        float sum = 0;
        for (int j = 0; j < INPUT_NODES; j++) {
            sum += input[j] * weights_ih[j][i];
        }
        hidden[i] = sigmoid(sum);
    }

    // Calculate the output layer
    for (int i = 0; i < OUTPUT_NODES; i++) {
        float sum = 0;
        for (int j = 0; j < HIDDEN_NODES; j++) {
            sum += hidden[j] * weights_ho[j][i];
        }
        output[i] = sigmoid(sum);
    }
}

This code may look a bit complex, but don’t worry, I’ll explain it:

  1. We define a three-layer neural network: an input layer (3 nodes), a hidden layer (4 nodes), and an output layer (2 nodes).
  2. <span>weights_ih</span>
    and <span>weights_ho</span> are the connection weights between neurons.
  3. <span>sigmoid</span>
    function is an activation function used to introduce non-linearity.
  4. <span>forward_propagation</span>
    function implements forward propagation, which is the computation process from input to output.
Neural Network Algorithms for Microcontrollers: Making Control More Humanized

Note: This is just a basic implementation; in actual applications, we also need to add training algorithms to adjust the weights.

4.

Practical Application Case

Imagine we use this neural network to control a small car to avoid obstacles. The input could be the distance data from three ultrasonic sensors, and the output would be the speed of the left and right wheels.

  1. We need to collect a large amount of data to train the network. For example, manually operating the car to avoid obstacles, recording sensor data and corresponding control commands.
  2. Use this data to train the neural network and adjust the weights.
  3. Burn the trained network parameters (i.e., those weights) into the microcontroller.

In this way, the car can autonomously make avoidance decisions based on the surrounding environment!

Neural Network Algorithms for Microcontrollers: Making Control More Humanized

5.

Common Problems and Solutions

  1. Insufficient Memory: Neural networks require storage for a large number of parameters. The solution is to optimize the network structure or use external memory.

  2. Slow Computation Speed: Consider using fixed-point arithmetic instead of floating-point, or using lookup tables instead of complex mathematical operations.

  3. Overfitting: This means the network may have only adapted to the training data, with poor generalization ability. The solution is to increase the diversity of the training data or use regularization techniques.

6.

Practical Suggestions

  1. Start with a small-scale network and gradually increase complexity.
  2. Use simulation tools for testing to avoid risks from direct debugging on hardware.
  3. Be patient! Debugging neural networks often requires repeated attempts.

Alright, that concludes today’s sharing. Although neural networks may seem profound, as long as you grasp the basic principles, implementing them on a microcontroller is not difficult. I hope this article can inspire your interest and encourage you to start adding some “intelligent” elements to your projects. Feel free to ask me any questions!

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