Least Mean Squares (LMS) Algorithm

The Least Mean Squares (LMS) algorithm is a method used to find the best-fit solution for predicting an outcome based on input data. It works by repeatedly adjusting its parameters (or weights) to minimize the difference between the predicted and actual results. Think of it like tuning a musical instrument: each adjustment aims to reduce the error margin. This process continues iteratively, gradually improving the accuracy of the predictions. LMS is widely used in areas like signal processing, adaptive filtering, and machine learning to develop systems that learn and adapt over time.