An improved algorithm for incremental extreme learning machine
Incremental extreme learning machine (I-ELM) randomly obtains the input weights and the hidden layer neuron bias during the training process. Some hidden nodes in the ELM play a minor role in the network outputs which may eventually increase the network complexity and even reduce the stability of th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-01-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/21642583.2020.1759156 |
| _version_ | 1818603175558512640 |
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| author | Shaojian Song Miao Wang Yuzhang Lin |
| author_facet | Shaojian Song Miao Wang Yuzhang Lin |
| author_sort | Shaojian Song |
| collection | DOAJ |
| description | Incremental extreme learning machine (I-ELM) randomly obtains the input weights and the hidden layer neuron bias during the training process. Some hidden nodes in the ELM play a minor role in the network outputs which may eventually increase the network complexity and even reduce the stability of the network. In order to avoid this issue, this paper proposed an enhanced method for the I-ELM which is referred to as the improved incremental extreme learning machine (II-ELM). At each learning step of original I-ELM, an additional offset k will be added to the hidden layer output matrix before computing the output weights for the new hidden node and analysed the existence of the offset k. Compared with several improved algorithms of ELM, the advantages of the II-ELM in the training time, the forecasting accuracy, and the stability are verified on several benchmark datasets in the UCI database. |
| first_indexed | 2024-12-16T13:19:00Z |
| format | Article |
| id | doaj.art-e72ac01a20084f679deb284685747b54 |
| institution | Directory Open Access Journal |
| issn | 2164-2583 |
| language | English |
| last_indexed | 2024-12-16T13:19:00Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj.art-e72ac01a20084f679deb284685747b542022-12-21T22:30:24ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832020-01-018130831710.1080/21642583.2020.17591561759156An improved algorithm for incremental extreme learning machineShaojian Song0Miao Wang1Yuzhang Lin2School of Electrical Engineering, Guangxi UniversitySchool of Electrical Engineering, Guangxi UniversityDepartment of Electrical and Computer Engineering, University of MassachusettsIncremental extreme learning machine (I-ELM) randomly obtains the input weights and the hidden layer neuron bias during the training process. Some hidden nodes in the ELM play a minor role in the network outputs which may eventually increase the network complexity and even reduce the stability of the network. In order to avoid this issue, this paper proposed an enhanced method for the I-ELM which is referred to as the improved incremental extreme learning machine (II-ELM). At each learning step of original I-ELM, an additional offset k will be added to the hidden layer output matrix before computing the output weights for the new hidden node and analysed the existence of the offset k. Compared with several improved algorithms of ELM, the advantages of the II-ELM in the training time, the forecasting accuracy, and the stability are verified on several benchmark datasets in the UCI database.http://dx.doi.org/10.1080/21642583.2020.1759156extreme learning machineincremental algorithmrandom hidden nodessingle-hidden layer feedforward neural networks |
| spellingShingle | Shaojian Song Miao Wang Yuzhang Lin An improved algorithm for incremental extreme learning machine Systems Science & Control Engineering extreme learning machine incremental algorithm random hidden nodes single-hidden layer feedforward neural networks |
| title | An improved algorithm for incremental extreme learning machine |
| title_full | An improved algorithm for incremental extreme learning machine |
| title_fullStr | An improved algorithm for incremental extreme learning machine |
| title_full_unstemmed | An improved algorithm for incremental extreme learning machine |
| title_short | An improved algorithm for incremental extreme learning machine |
| title_sort | improved algorithm for incremental extreme learning machine |
| topic | extreme learning machine incremental algorithm random hidden nodes single-hidden layer feedforward neural networks |
| url | http://dx.doi.org/10.1080/21642583.2020.1759156 |
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