RBF Neural Network for Classification with MATLAB Implementation

In this documentation, I would like to share detailed information about implementing RBF neural networks for classification using MATLAB. The program demonstrates high flexibility, where you can adapt it to your specific datasets by modifying relevant parameters and configurations.

First, let me briefly explain the fundamental principles of RBF neural networks. RBF stands for Radial Basis Function, which serves as a powerful tool for handling nonlinear classification problems. The network architecture consists of three layers: input layer, hidden layer, and output layer. The input layer receives feature vectors from the dataset, the hidden layer employs radial basis functions to transform inputs into a high-dimensional feature space, and the output layer performs classification based on these transformed features. The key algorithm involves calculating Euclidean distances between input vectors and hidden layer centers, followed by Gaussian activation function implementation.

Now, let's examine the MATLAB program structure. Initially, you need to prepare your dataset and partition it into training and testing sets using functions like cvpartition or manual splitting. Subsequently, you must configure critical parameters such as the number of hidden layer neurons (using newrb or newrbe functions) and the spread parameter controlling radial basis function width. These parameters significantly impact network performance, requiring experimental tuning through cross-validation techniques.

Next, you'll write code to construct and train the RBF neural network. MATLAB's Neural Network Toolbox provides essential functions like newrb for iterative network creation or newrbe for exact interpolation. During training, the network optimizes parameters through pseudo-inverse calculations (for output weights) and k-means clustering (for center selection) to minimize classification error on training samples, ensuring accurate generalization to unseen data during testing.

Finally, you can utilize the trained network for classification predictions. Feed your test samples into the network using the sim function and analyze output layer results. The classification decision typically involves selecting the output neuron with the highest activation value, which you can implement through max function operations or threshold-based approaches.

I hope this information proves valuable for your implementation! Should you have any technical questions regarding parameter optimization or code customization, please feel free to ask for further clarification.