Detailed Implementation of GMDH Neural Network
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Detailed Documentation
This document provides a detailed breakdown of the GMDH (Group Method of Data Handling) neural network implementation process. The demo serves as the main program, designed to demonstrate the functionality and performance of the GMDH neural network through practical examples. The implementation follows a structured approach: data preprocessing, layer-by-layer network growth using polynomial activation functions, and model validation through external criteria. Key algorithms include combinatorial selection of input variables and iterative complexity reduction through self-organization principles. The program showcases how GMDH handles time-series forecasting and multivariate regression tasks with automatic structure optimization. Through this comprehensive implementation, users gain deeper understanding of GMDH's working mechanism, including its inductive learning approach and applications in predictive modeling scenarios.
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