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Cognitive Neurodynamics
Erschienen in: Cognitive Neurodynamics 1/2016

01.02.2016 | Research Article

A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

verfasst von: Zhiyong Yang, Taohong Zhang, Dezheng Zhang

Erschienen in: Cognitive Neurodynamics | Ausgabe 1/2016

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Abstract

Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.
Vorheriger Artikel Neural network modelling of the influence of channelopathies on reflex visual attention
Nächster Artikel New stability criterion of neural networks with leakage delays and impulses: a piecewise delay method

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Fußnoten
1
To make \(F_b\) change dynamically, we set \(F_b = 0.9 -\frac{(0.9-0.4)t}{n_t}\), where t is the current iteration number, \(n_t\) is the total iteration number.
 
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Metadaten
Titel
A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training
verfasst von
Zhiyong Yang
Taohong Zhang
Dezheng Zhang
Publikationsdatum
01.02.2016
Verlag
Springer Netherlands
Erschienen in
Cognitive Neurodynamics / Ausgabe 1/2016
Print ISSN: 1871-4080
Elektronische ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-015-9358-9

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