id: 06059008
dt: a
an: 06059008
au: Han, Fei; Yao, Hai-Fen; Ling, Qing-Hua
ti: An improved extreme learning machine based on particle swarm optimization.
so: Huang, De-Shuang (ed.) et al., Bio-inspired computing and applications. 7th
    international conference on intelligent computing, ICIC 2011,
    Zhengzhou, China, August 11‒14. 2011. Revised selected papers.
    Berlin: Springer (ISBN 978-3-642-24552-7/pbk). Lecture Notes in
    Computer Science 6840. Lecture Notes in Bioinformatics, 699-704 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: extreme learning machine; particle swarm optimization; generalization
    performance
ci: 
li: doi:10.1007/978-3-642-24553-4_92
ab: Summary: Traditional 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.
rv: