Universal approximation of extreme learning machine with adaptive growth of hidden nodes.
IEEE Trans Neural Netw Learn Syst
; 23(2): 365-71, 2012 Feb.
Article
en En
| MEDLINE
| ID: mdl-24808516
ABSTRACT
Extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks which need not be neuron-like and perform well in both regression and classification applications. In this brief, we propose an ELM with adaptive growth of hidden nodes (AG-ELM), which provides a new approach for the automated design of networks. Different from other incremental ELMs (I-ELMs) whose existing hidden nodes are frozen when the new hidden nodes are added one by one, in AG-ELM the number of hidden nodes is determined in an adaptive way in the sense that the existing networks may be replaced by newly generated networks which have fewer hidden nodes and better generalization performance. We then prove that such an AG-ELM using Lebesgue p-integrable hidden activation functions can approximate any Lebesgue p-integrable function on a compact input set. Simulation results demonstrate and verify that this new approach can achieve a more compact network architecture than the I-ELM.
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MEDLINE
Asunto principal:
Algoritmos
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Reconocimiento de Normas Patrones Automatizadas
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Modelos Estadísticos
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Redes Neurales de la Computación
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Dinámicas no Lineales
Tipo de estudio:
Risk_factors_studies
Idioma:
En
Revista:
IEEE Trans Neural Netw Learn Syst
Año:
2012
Tipo del documento:
Article