Gray Correlation Degree Algorithm Implementation in MATLAB

This article provides a comprehensive examination of the gray correlation degree algorithm implementation using MATLAB, discussing both its practical advantages and limitations. The algorithm's distinctive feature lies in its ability to model and predict nonlinear systems without requiring preliminary linearization of data. This capability makes it widely applicable across diverse fields including finance, environmental science, and civil engineering. From an implementation perspective, we will demonstrate key MATLAB functions for data normalization, correlation coefficient calculation, and degree analysis. The implementation typically involves steps such as data preprocessing using z-score normalization, computing correlation coefficients through difference sequences, and final degree calculation using weighted averaging methods. We will also introduce optimization techniques to enhance algorithm accuracy and computational efficiency, including improved weighting methods and parallel computing approaches for large datasets. Finally, we will explore current research directions and future development trends in gray system theory, with specific attention to integration with machine learning algorithms and real-time processing capabilities. This article aims to provide valuable insights and stimulate further research considerations for readers working with uncertain systems and limited data scenarios.