MATLAB Implementation of Differential Evolution for Optimizing Extreme Learning Machine Neural Networks

This MATLAB source code implements the integration of Differential Evolution (DE) optimization with Extreme Learning Machine (ELM) neural networks to enhance network performance and prediction accuracy. The implementation consists of two main algorithmic components: 1. Differential Evolution Algorithm: A population-based optimization technique that mimics biological evolution processes through mutation, crossover, and selection operations. The code implements DE's parameter optimization mechanism to fine-tune ELM's hidden layer parameters, including weights and biases. 2. Extreme Learning Machine: A fast-learning single-hidden-layer feedforward neural network where the hidden layer parameters are randomly assigned and the output weights are analytically determined. The code implements ELM's efficient Moore-Penrose pseudoinverse calculation for output weight determination. Key implementation features include: - Population initialization with random parameter vectors - Mutation strategies using difference vectors between population members - Crossover operations to generate trial vectors - Selection mechanisms based on fitness evaluation - ELM network training with optimized hidden node parameters - Performance metrics calculation for model validation The integration workflow follows: DE optimizes ELM's hidden layer parameters by minimizing prediction error, then the optimized ELM model undergoes rapid training using least-squares solution. This combined approach yields a robust neural network model that maintains ELM's training efficiency while achieving superior generalization performance through DE's global optimization capabilities. This practical tool provides researchers and developers with a ready-to-use implementation for testing and applying optimized ELM networks in MATLAB environment, supporting applications in regression, classification, and time-series prediction tasks.