RBF Neural Networks for Regression

RBF neural networks are a type of feedforward neural network widely used for function approximation and regression problems. Their core concept involves transforming input space through nonlinear radial basis functions, making them particularly suitable for handling complex nonlinear relationships.

Implementing RBF neural network regression in MATLAB typically involves these key steps: First, prepare training data including input features and corresponding target output values. Then construct the network using either newrb or newrbe functions - both automatically determine the number of hidden layer nodes. The newrb function gradually adds neurons based on error targets, while newrbe uses all samples as center points.

Key parameters adjusted during network training include: SPREAD (extension speed) affecting the width of radial basis functions (larger values create smoother functions); GOAL (target error) controlling model accuracy; and MN (maximum neurons) limiting network size to prevent overfitting. In practice, these parameters can be optimized through cross-validation techniques.

After training, use the sim function for predicting new data. MATLAB's encapsulation eliminates the need for manual weight calculation and basis function transformation, allowing developers to focus on data preprocessing and parameter tuning. For different regression tasks, the network structure remains unchanged - simply replace the dataset for quick application migration.

The method's advantages include fast training speed and strong local approximation capability, though it shows sensitivity to center point selection and parameter configuration. Practical implementations often incorporate data normalization and monitor test set performance to prevent overfitting.