Error Learning Algorithm for Artificial Neural Network Perceptrons

This article introduces the error learning algorithm for artificial neural network perceptrons, which enables the learning process of single-layer perceptrons. A perceptron serves as a fundamental artificial neuron model capable of classifying input data through learned adjustments of weights and thresholds. The error learning algorithm represents a widely-used training method for perceptrons, updating weights and thresholds based on the difference between the perceptron's actual output and expected output. The core concept involves iteratively reducing errors until meeting predefined accuracy thresholds. From an implementation perspective, the algorithm typically involves: - Initializing random weights and bias terms - Calculating weighted sums of inputs using matrix operations - Applying activation functions (commonly step or sign functions) - Computing error terms by comparing outputs with target values - Updating weights using the formula: new_weight = old_weight + learning_rate * error * input Through the error learning algorithm, perceptrons can continuously optimize their classification capabilities, achieving progressively more accurate learning outcomes. The algorithm's iterative nature ensures gradual convergence toward optimal weight configurations when implemented with appropriate learning rates and stopping criteria.