Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural Network
The Generalized Adaptive Resonance Theory (GART) model is a supervised online learning neural network based on an integration of Adaptive Resonance Theory (ART) and the Generalized Regression Neural Network (GRNN). It is capable of online learning, and is suitable for undertaking both classification...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8801937/ |
| _version_ | 1818380785951965184 |
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| author | Shen Yuong Wong Keem Siah Yap Chee Peng Lim Eric Wai Ming Lee |
| author_facet | Shen Yuong Wong Keem Siah Yap Chee Peng Lim Eric Wai Ming Lee |
| author_sort | Shen Yuong Wong |
| collection | DOAJ |
| description | The Generalized Adaptive Resonance Theory (GART) model is a supervised online learning neural network based on an integration of Adaptive Resonance Theory (ART) and the Generalized Regression Neural Network (GRNN). It is capable of online learning, and is suitable for undertaking both classification and regression problems. In this paper, we further enhance GART (EGART) with four improvements to formulate a new EGART model. Three operating strategies for the EGART model to undertake regression problems are suggested. The first operating strategy is a fully online learning EGART model. The second operating strategy involves Ordering Algorithm for determining the presentation sequence of training samples during the initial training of EGART model. This strategy is considered as offline learning because a set of data samples must be available for the Ordering Algorithm to compute the best presentation sequence (hereinafter denoted as Ordered-EGART). The third operating strategy aims to demonstrate online learning capability of EART model (the first operating strategy) can still be resumed after training on the Ordered-EGART. It is most suitable for applications with a set of ready data samples and their sequences are predetermined by Ordering Algorithm prior to training of EGART model in offline mode, and triggers online learning when more new data samples become available (hereinafter denoted as IO-EGART). A series of experiments with five benchmark data sets from various application domains is conducted to assess and compare the effectiveness of the EGART model and three operating strategies with those of other methods published in literature as well as two fire safety engineering problems, i.e., predicting the thermal interface height in a single compartment fire and evacuation times in the event of fire. The results and comparisons with other approaches positively demonstrate the efficacy and applicability of EGART model as a useful data regression model for tackling fire safety engineering problems. |
| first_indexed | 2024-12-14T02:24:12Z |
| format | Article |
| id | doaj.art-fa62bc4f15b34f948f900e03e219101d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:24:12Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fa62bc4f15b34f948f900e03e219101d2022-12-21T23:20:26ZengIEEEIEEE Access2169-35362019-01-01711643811645210.1109/ACCESS.2019.29354548801937Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural NetworkShen Yuong Wong0https://orcid.org/0000-0001-6027-5346Keem Siah Yap1https://orcid.org/0000-0002-5518-5132Chee Peng Lim2Eric Wai Ming Lee3Department of Electrical and Electronics Engineering, Xiamen University Malaysia, Sepang, MalaysiaDepartment of Electrical and Electronics Engineering, Universiti Tenaga Nasional, Kajang, MalaysiaInstitute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC, AustraliaDepartment of Architecture and Civil Engineering, City University of Hong Kong, Hong KongThe Generalized Adaptive Resonance Theory (GART) model is a supervised online learning neural network based on an integration of Adaptive Resonance Theory (ART) and the Generalized Regression Neural Network (GRNN). It is capable of online learning, and is suitable for undertaking both classification and regression problems. In this paper, we further enhance GART (EGART) with four improvements to formulate a new EGART model. Three operating strategies for the EGART model to undertake regression problems are suggested. The first operating strategy is a fully online learning EGART model. The second operating strategy involves Ordering Algorithm for determining the presentation sequence of training samples during the initial training of EGART model. This strategy is considered as offline learning because a set of data samples must be available for the Ordering Algorithm to compute the best presentation sequence (hereinafter denoted as Ordered-EGART). The third operating strategy aims to demonstrate online learning capability of EART model (the first operating strategy) can still be resumed after training on the Ordered-EGART. It is most suitable for applications with a set of ready data samples and their sequences are predetermined by Ordering Algorithm prior to training of EGART model in offline mode, and triggers online learning when more new data samples become available (hereinafter denoted as IO-EGART). A series of experiments with five benchmark data sets from various application domains is conducted to assess and compare the effectiveness of the EGART model and three operating strategies with those of other methods published in literature as well as two fire safety engineering problems, i.e., predicting the thermal interface height in a single compartment fire and evacuation times in the event of fire. The results and comparisons with other approaches positively demonstrate the efficacy and applicability of EGART model as a useful data regression model for tackling fire safety engineering problems.https://ieeexplore.ieee.org/document/8801937/Adaptive resonance theoryfire evacuation timegeneral regression neural networkordering algorithmregressionthermal interface height |
| spellingShingle | Shen Yuong Wong Keem Siah Yap Chee Peng Lim Eric Wai Ming Lee Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural Network IEEE Access Adaptive resonance theory fire evacuation time general regression neural network ordering algorithm regression thermal interface height |
| title | Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural Network |
| title_full | Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural Network |
| title_fullStr | Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural Network |
| title_full_unstemmed | Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural Network |
| title_short | Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural Network |
| title_sort | hybrid data regression model based on the generalized adaptive resonance theory neural network |
| topic | Adaptive resonance theory fire evacuation time general regression neural network ordering algorithm regression thermal interface height |
| url | https://ieeexplore.ieee.org/document/8801937/ |
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