Modified ELM MATLAB Algorithm with Enhanced Performance

In this implementation, I have modified the original ELM MATLAB algorithm. The enhanced version shows remarkable computational speed improvement when dealing with more than 3 neurons. The core principle involves utilizing function-generated column matrices for efficient matrix operations. ELM represents a fast neural network algorithm that, through my comparative analysis, demonstrates superior performance over popular alternatives like Backpropagation (BP) and Support Vector Machines (SVM) in terms of both execution speed and result quality. The algorithm operates within MATLAB environment and supports comprehensive testing across all benchmark datasets. Key implementation enhancements include: - Optimized column matrix generation using built-in MATLAB functions - Improved matrix multiplication techniques for hidden layer computations - Efficient weight initialization methods reducing computational overhead Additionally, the algorithm allows parameter tuning for further performance optimization, including adjustment of hidden layer neurons, activation functions, and regularization parameters. This modified ELM MATLAB implementation not only accelerates computation but also delivers improved results with broader applicability across various dataset types and problem domains.