An improved extreme learning machine with adaptive growth of hidden nodes based on particle swarm optimization

Extreme learning machines (ELMs) for generalized single-hidden-layer feedforward networks which perform well in both regression and classification applications have caused a lot of attention. To obtain compact network architecture with better generalization performance, an improved ELM with adaptive growth of hidden nodes (AG-ELM) combined with particle swarm optimization (PSO) is proposed in this study. PSO is used to select the optimal weights and biases to overcome the deficiency of the standard AG-ELM. All parameters in one network are represented by one particle in PSO, and the dimension of the particle increases in the training process. Simulation results on various test problems verify that the proposed algorithm achieves more compact network architecture and has better generalization performance with less steps than classical AG-ELM.