Generalized Regression Neural Network (GRNN) Implementation Example

This article presents an educational implementation example of Generalized Regression Neural Network (GRNN) designed to help readers better understand and master regression tasks using this architecture. The example demonstrates key implementation aspects including the network's probabilistic foundation based on Parzen window density estimation, the radial basis function (RBF) layer implementation, and the regression layer calculation. We will examine the network's advantages such as fast training speed (single-pass learning) and smooth function approximation capabilities, while also discussing limitations like memory-intensive computation and potential overfitting with improper smoothing parameter selection. The implementation typically involves configuring the spread parameter (sigma) that controls the RBF width, organizing training samples as pattern neurons, and calculating outputs using normalized weighted sums. Possible improvements include dynamic sigma optimization through cross-validation and dimensionality reduction techniques for large datasets. Through this practical example, readers will gain deeper insights into GRNN's working mechanism and learn to apply it more flexibly in real-world scenarios with appropriate parameter tuning and data preprocessing strategies.