Predictive Commands for Four Neural Network Types: BP, Elman, RBF, and GRNN

This prediction command set supports four major neural network architectures: Backpropagation (BP), Elman, Radial Basis Function (RBF), and General Regression Neural Network (GRNN). Each network type employs distinct computational approaches for data prediction and analysis. The BP network implements multi-layer error backpropagation with gradient descent optimization, suitable for general pattern recognition tasks. The Elman network features recurrent connections with context units, ideal for time-series forecasting through its internal memory mechanism. RBF networks utilize Gaussian kernel functions for nonlinear mapping, providing efficient interpolation capabilities. GRNN operates based on probability density estimation, offering smooth regression surfaces for continuous data prediction. Implementation can be structured using MATLAB's Neural Network Toolbox functions like `feedforwardnet` (for BP), `elmannet` (for Elman recurrent networks), `newrbe` (for exact RBF implementation), and custom GRNN coding using radial basis layers with probabilistic normalization. Each command accepts input data matrices and returns predicted outputs with configurable parameters for hidden layers, spread factors (RBF/GRNN), and training iterations.