MATLAB Implementation of GMDH Neural Network Algorithm

This implementation provides a MATLAB-based GMDH (Group Method of Data Handling) neural network solution. GMDH is a combinatorial learning method primarily designed for solving multivariate regression problems. The core algorithm operates by partitioning input datasets into distinct subsets and applying individual models to each subset. These component models are then systematically combined into a larger composite model to enhance prediction accuracy. The implementation leverages MATLAB's computational capabilities and built-in functions to create an efficient GMDH architecture. Key features include automated data segmentation using cross-validation techniques, recursive model generation through polynomial activation functions, and optimal model selection based on predefined criteria such as minimum prediction error. The code structure includes main functions for data preprocessing, layer-wise network construction, and model evaluation. Critical algorithms implemented include: forward propagation with partial polynomial descriptions, iterative layer addition with stopping criteria, and ensemble model aggregation using weighting schemes. The implementation utilizes MATLAB's matrix operations for efficient computation and includes visualization tools for monitoring network growth and performance metrics. By utilizing this implementation, users can rapidly construct and deploy GMDH networks for various regression applications, benefiting from MATLAB's optimized numerical computation and built-in statistical tools for robust model validation and performance analysis.