Handwritten Digit Recognition Based on Matlab RBF
With the rapid development of artificial intelligence, image recognition technology has been widely used. Among them, handwritten digit recognition, as an important research direction in the field of image recognition, has always been concerned by academia and industry. This paper will realize handwritten digit recognition based on Matlab RBF neural network algorithm, and analyze and explain the algorithm in detail.
- Handwritten Digit Recognition Dataset
The handwritten digit recognition dataset is a public dataset in the field of machine learning. The dataset contains 60,000 training samples and 10,000 testing samples, each of which is a 28x28 pixel grayscale image. In Matlab, this dataset can be imported using the built-in datasets package.
- RBF neural network algorithm principle
The RBF neural network is a feed-forward neural network whose basic structure consists of an input layer, a hidden layer and an output layer. The hidden layer of RBF neural network is generally activated by radial basis function, and the specific activation function selection can be adjusted according to the actual situation.
The training process of RBF neural network is realized by minimizing the output error. Usually, a training algorithm based on the least squares method or a gradient descent method is used for parameter update. In this article, we will adopt a least squares based training algorithm.
- Realization of Handwritten Digit Recognition with RBF Neural Network
In this section, we will model the handwritten digit recognition problem and use the RBF neural network toolbox in Matlab for model training and testing.
First, we need to process the input data and expand the 28x28 grayscale image data into a 784-dimensional vector. This step can be achieved through the reshape function:
trainImages = double(