Extreme Learning Machine Classifier Based on Novel Particle Swarm Optimization Algorithm

Abstract

Aiming at the problem of low classification accuracy caused by random input weights and hidden layer bias in ELM (Extreme Learning Machines, ELM), a method based on IPSO (Improved Particle Swarm Optimization, IPSO) to optimize the network classification model of Extreme Learning Machines is proposed. Firstly, the nonlinear inertia weight and dynamic acceleration factor are used to balance the exploration and mining of the PSO algorithm; Secondly, the variables of the ELM model are enhanced by IPSO to reduce the influence caused by strong volatility; Finally, simulation results show that compared with SVM, ELM, GA-ELM, IPSO-ELM could effectively improve the precision of classification prediction.