BP neural network handwritten digit recognition based on MATLAB GUI

BP neural network handwritten digit recognition based on MATLAB GUI

In this article, we will introduce how to use MATLAB GUI and BP (Backpropagation) neural network to realize handwritten digit recognition. BP neural network is a commonly used artificial neural network that is often used for pattern recognition and classification tasks. We will use MATLAB's GUI interface to create a user-friendly application that enables users to handwrite numbers and have them recognized through a BP neural network.

First, we need to prepare some training data. We will use the MNIST dataset, a widely used dataset containing images of handwritten digits. The MNIST data set contains 60,000 training samples and 10,000 test samples, each sample is a 28x28 pixel grayscale image. We will use training samples to train the BP neural network and test samples to evaluate its performance.

Next, we will create a MATLAB GUI application. First, we need to create a window and draw a canvas on which we can handwrite numbers. We can do this using MATLAB's plotting functions. Below is sample code to create a GUI window and draw the canvas:

function digitRecognitionGUI
    % 创建窗口
    fig = figure('Name','手写数字识别',<