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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2018

27.07.2017

Fast Learning Network with Parallel Layer Perceptrons

verfasst von: Guoqiang Li, Xiaobin Qi, Bin Chen, Yunpeng Ma, Peifeng Niu, Zhiwang Chen

Erschienen in: Neural Processing Letters | Ausgabe 2/2018

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Abstract

This paper proposes a novel artificial neural network called Parallel Layer Perceptron Fast Learning Network (PLP-FLN). In PLP-FLN, a parallel single hidden layer feed-forward neural network is added on the basis of Fast Learning Network (FLN) which is an improved Extreme Learning Machine (ELM). Input weights and hidden layer biases are randomly generated. The weights connect the output nodes and the input nodes, and the weights connect the output nodes and the hidden nodes are analytically determined based on least squares methods. In order to test the PLP-FLN validity, this paper compared it with ELM, FLN, Kernel ELM and Incremental ELM through 12 regression applications and 7 classification problems. By comparing the experimental results, it shows that the PLP-FLN with much more compact networks have demonstrated better approximations, classification performances and generalization ability.
Vorheriger Artikel Supervised Dictionary Learning with Smooth Shrinkage for Image Denoising
Nächster Artikel A Novel Greedy Randomized Dynamic Ensemble Selection Algorithm

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Metadaten
Titel
Fast Learning Network with Parallel Layer Perceptrons
verfasst von
Guoqiang Li
Xiaobin Qi
Bin Chen
Yunpeng Ma
Peifeng Niu
Zhiwang Chen
Publikationsdatum
27.07.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9667-6

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