Evolutionary Extreme Learning Machine – Based on Particle Swarm Optimization

Abstract

A new off-line learning method of single-hidden layer feed-forward neural networks (SLFN) called Extreme Learning Machine (ELM) was introduced by Huang et al. [1, 2, 3, 4] . ELM is not the same as traditional BP methods as it can achieve good generalization performance at extremely fast learning speed. In ELM, the hidden neuron parameters (the input weights and hidden biases or the RBF centers and impact factors) were pre-assigned randomly so there may be a set of non-optimized parameters that avoid ELM achieving global minimum in some applications. Adopting the ideas in [5] that a single layer feed-forward neural network can be trained using a hybrid approach which takes advantages of both ELM and the evolutionary algorithm, this paper introduces a new kind of evolutionary algorithm called particle swarm optimization (PSO) which can train the network more suitable for some prediction problems using the ideas of ELM.