GRNN-Based Data Prediction for Freight Volume Analysis

This case study utilizes data.mat containing p and t datasets with 13 samples each, representing freight volume and related variables from 1996-2008. The first 12 samples serve as training data while the final sample is used for prediction, implementing a GRNN neural network with MATLAB's newgrnn function and radial basis function computation.