RBF Neural Networks for Pattern Classification

RBF (Radial Basis Function) neural networks can be effectively employed for pattern classification tasks. This implementation utilizes MATLAB 6.5 platform, where the RBF network architecture consists of three layers: input layer, hidden layer with radial basis activation functions, and output layer for classification decisions. Key implementation aspects include: - Radial basis function centers selection using clustering algorithms like k-means - Gaussian activation functions calculating distances between input vectors and centers - Output layer weights determination through linear regression or gradient descent - Network training involving center adjustment and weight optimization The MATLAB implementation typically uses functions like newrbf for network creation and train for supervised learning, with parameters adjustable for different pattern recognition requirements. The RBF network's localized response characteristics make it particularly suitable for classification problems with non-linear decision boundaries.