Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods
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 hybr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-01-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/00051144.2019.1570642 |
| _version_ | 1819074500718755840 |
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| author | Di Wu Zong Shun Qu Feng Jiao Guo Xiao Lin Zhu Qin Wan |
| author_facet | Di Wu Zong Shun Qu Feng Jiao Guo Xiao Lin Zhu Qin Wan |
| author_sort | Di Wu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T18:10:30Z |
| format | Article |
| id | doaj.art-5b8d544ae56145408be57aade8b5a4b5 |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-12-21T18:10:30Z |
| publishDate | 2019-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-5b8d544ae56145408be57aade8b5a4b52022-12-21T18:54:48ZengTaylor & Francis GroupAutomatika0005-11441848-33802019-01-01601485710.1080/00051144.2019.15706421570642Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methodsDi Wu0Zong Shun Qu1Feng Jiao Guo2Xiao Lin Zhu3Qin Wan4College of Electrical and Information Engineering, Hunan Institute of EngineeringCollege of Electrical and Information Engineering, Hunan Institute of EngineeringCollege of Electrical and Information Engineering, Hunan Institute of EngineeringCollege of Electrical and Information Engineering, Hunan Institute of EngineeringCollege of Electrical and Information Engineering, Hunan Institute of EngineeringFocussing 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.http://dx.doi.org/10.1080/00051144.2019.1570642Extreme learning machine (ELM)kernel incremental extreme learning machine (KIELM)differential evolution (DE)multiple population grey wolf optimization methods (MPGWO)hybrid intelligence (HI) |
| spellingShingle | Di Wu Zong Shun Qu Feng Jiao Guo Xiao Lin Zhu Qin Wan Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods Automatika Extreme learning machine (ELM) kernel incremental extreme learning machine (KIELM) differential evolution (DE) multiple population grey wolf optimization methods (MPGWO) hybrid intelligence (HI) |
| title | Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods |
| title_full | Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods |
| title_fullStr | Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods |
| title_full_unstemmed | Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods |
| title_short | Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods |
| title_sort | hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods |
| topic | Extreme learning machine (ELM) kernel incremental extreme learning machine (KIELM) differential evolution (DE) multiple population grey wolf optimization methods (MPGWO) hybrid intelligence (HI) |
| url | http://dx.doi.org/10.1080/00051144.2019.1570642 |
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