Widrow-Hoff Learning Rule

The Widrow-Hoff Learning Rule, also known as the Least Mean Squares (LMS) algorithm, is a method used for training neural networks to make accurate predictions. It adjusts the network’s weights (connections) gradually by comparing its outputs to the correct answers, then tweaking the weights to reduce the difference. This process repeats, incrementally improving accuracy over time. Think of it as a feedback system that learns from mistakes, slowly fine-tuning itself to better match the desired results, making it effective for tasks like pattern recognition and signal processing.