An Improved Incremental Error Minimized Extreme Learning Machine for Regression Problem Based on Particle Swarm Optimization

Error minimized extreme learning machine (EM-ELM) is a simple and efficient approach to determine the number of hidden nodes. However, EM-ELM lays much emphasis on the convergence accuracy, which may obtain a single-hidden-layer feedforward neural network (SLFN) with good convergence performance but bad condition. In this paper, an effective approach based on error minimized ELM and particle swarm optimization (PSO) is proposed to automatically determine the structure of SLFN for regression problem. In the new method, the hidden node optimized by PSO is added to the SLFN one by one. Experimental results verify that the proposed algorithm achieves better generalization performance with better condition than other constructive ELM.