Abstract
AbstractTraditional extreme learning machine (ELM) may require high number of hidden neurons and lead to ill-condition problem due to the random determination of the input weights and hidden biases. In this paper, we use a modified particle swarm optimization (PSO) algorithm to select the input weights and hidden biases of single-hidden-layer feedforward neural networks (SLFN) and Moore–Penrose (MP) generalized inverse to analytically determine the output weights. The modified PSO optimizes the input weights and hidden biases according to not only the root mean squared error on validation set but also the norm of the output weights. The proposed algorithm has better generalization performance than other ELMs and its conditioning is also improved.KeywordsExtreme learning machineparticle swarm optimizationgeneralization performance
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