Neural Network ELM Algorithm

This document introduces the Extreme Learning Machine (ELM) algorithm for neural networks. Compared to traditional backpropagation (BP) and Support Vector Machine (SVM) algorithms, ELM achieves significantly faster computation speeds while delivering comparable or superior accuracy. The algorithm is implemented in MATLAB environment, where I have enhanced the original ELM implementation with several key improvements: expanded function support through adaptable activation functions (sigmoid, radial basis, etc.), and automated classification matrix generation that streamlines data preprocessing. The core implementation utilizes MATLAB's matrix operations for efficient hidden layer output calculation H = g(X*W + b), where g represents the activation function, followed by the analytical solution for output weights β = pinv(H)*T. Additional advantages include straightforward implementation architecture, minimal parameter tuning requirements, and excellent generalization capabilities. Overall, ELM represents a powerful machine learning algorithm with broad applicability across various domains including pattern recognition, regression analysis, and classification tasks.