Data Prediction Using RLS Algorithm and MATLAB Implementation
The Recursive Least Squares (RLS) algorithm, originally proposed by the renowned mathematician Gauss in 1795, represents a classical data processing methodology. Gauss established that when inferring unknown parameters from observed data, the most probable values are those that minimize the sum of squared differences between actual observations and calculated values, weighted by their precision measures - this forms the foundation of the famous least squares method. Widely applied in adaptive signal filtering analysis, the RLS algorithm offers rapid convergence and insensitivity to eigenvalue dispersion in autocorrelation matrices. However, it demands substantial computational resources. This chapter focuses on RLS-based data prediction techniques and their practical MATLAB implementation, including key algorithmic components and code optimization strategies.