a multiple hidden layers extreme learning machine method and its application

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2017
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Abstract
Extreme learning machine (ELM) is a rapid learning algorithm of the single-hidden-layer feedforward neural network, which randomly initializes the weights between the input layer and the hidden layer and the bias of hidden layer neurons and finally uses the least-squares method to calculate the weights between the hidden layer and the output layer. This paper proposes a multiple hidden layers ELM (MELM for short) which inherits the characteristics of parameters of the first hidden layer. The parameters of the remaining hidden layers are obtained by introducing a method (make the actual output zero error approach the expected hidden layer output). Based on the MELM algorithm, many experiments on regression and classification show that the MELM can achieve the satisfactory results based on average precision and good generalization performance compared to the two-hidden-layer ELM (TELM), the ELM, and some other multilayer ELM.
Reference Key
xiao2017mathematicala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Dong Xiao;Beijing Li;Yachun Mao
Journal journal of power sources
Year 2017
DOI
10.1155/2017/4670187
URL
http://dx.doi.org/10.1155/2017/4670187
Keywords
time-varying delay civil engineering (general)mathematics engineering (general)

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