Freight Volume Prediction Using Generalized Regression Neural Network

This source code implements freight volume prediction using Generalized Regression Neural Network (GRNN) methodology. The implementation includes fully executable code that can be directly compiled and run, accompanied by extensive code comments that elucidate the implementation workflow and functional components. The GRNN algorithm employs probabilistic density function estimation through radial basis functions, providing smooth interpolation for continuous variable prediction. Key functions include data normalization, network parameter optimization, and prediction accuracy validation modules. Using this codebase, developers can gain deeper insights into freight forecasting principles and methodologies, while having the flexibility to modify and optimize the implementation according to specific requirements. The code serves as an excellent reference resource for both research purposes and practical applications in transportation logistics and predictive analytics.