Original scientific paper
https://doi.org/10.1080/00051144.2019.1570642
Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods
Di Wu
Zong Shun Qu
Feng Jiao Guo
Xiao Lin Zhu
Qin Wan
Abstract
Focussing on the problem that redundant nodes in the kernel incremental extreme learning machine (KI-ELM) which leads to ineffective iteration increase and reduce the learning efficiency, a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM) based on a hybrid intelligent algorithms and kernel incremental extreme learning machine is proposed. At first, hybrid intelligent algorithms are proposed based on differential evolution (DE) and multiple population grey wolf optimization (MPGWO) methods which used to optimize the hidden layer neuron parameters and then to determine the effective hidden layer neurons number. The learning efficiency of the algorithm is improved by reducing the network complexity. Then, we bring in the deep network structure to the kernel incremental extreme learning machine to extract the original input data layer by layer gradually. The experiment results show that the HI-DKIELM methods proposed in this paper with more compact network structure have higher prediction accuracy and better ability of generation compared with other ELM methods.
Keywords
Extreme learning machine (ELM); kernel incremental extreme learning machine (KIELM); differential evolution (DE); multiple population grey wolf optimization methods (MPGWO; hybrid intelligence (HI)
Hrčak ID:
239754
URI
Publication date:
26.2.2019.
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