Hidden Node Optimization for Extreme Learning Machine

The number of hidden nodes is a critical factor for the generalization of ELM. Generally, it is heavy for time consumption to obtain the optimal number of hidden nodes with trial-and-error. A novel algorithm is proposed to optimize the hidden node number to guarantee good generalization, which employs the PSO in the optimization process with structural risk minimization principle. The simulation results indicate our algorithm for the optimal number of hidden nodes is reasonable and feasible with 6 datasets on benchmark problems by the accuracy comparisons.