A functional link neural network with modified cuckoo search for prediction tasks
The impact of temperature, relative humidity and ozone changes bring a sharp warming climate. These changes can cause extreme consequences such as floods, hurricanes, heat waves and droughts. Therefore, prediction of temperature and relative humidity is an important factor to measure the environment...
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| Format: | Thesis |
| Language: | English English English |
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2017
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| Online Access: | http://eprints.uthm.edu.my/865/ http://eprints.uthm.edu.my/865/1/24p%20SITI%20ZULAIKHA%20ABU%20BAKAR.pdf http://eprints.uthm.edu.my/865/2/SITI%20ZULAIKHA%20ABU%20BAKAR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/865/3/SITI%20ZULAIKHA%20ABU%20BAKAR%20WATERMARK.pdf |
| _version_ | 1848887314615369728 |
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| author | Abu Bakar, Siti Zulaikha |
| author_facet | Abu Bakar, Siti Zulaikha |
| author_sort | Abu Bakar, Siti Zulaikha |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | The impact of temperature, relative humidity and ozone changes bring a sharp warming climate. These changes can cause extreme consequences such as floods, hurricanes, heat waves and droughts. Therefore, prediction of temperature and relative humidity is an important factor to measure the environmental changes. Neural network, especially the Multi-Layer Perceptron (MLP) which uses Back Propagation algorithm (BP) as a supervised learning method, has been successfully applied in various problems for meteorological prediction tasks. However, this architecture has still been facing problems which the convergence rate is very low due to the multi layering topology of the network. Thus, this research proposed an implementation of Functional Link Neural Network (FLNN) which composed of a single layer of tunable weight trained with the Modified Cuckoo Search algorithm (MCS). The proposed approach was used to predict the daily temperatures, relative humidity and ozone data. Extensive simulation results have been compared with standard MLP trained with the BP, FLNN with BP and FLNN with CS. Promising results have shown that the proposed model has successfully out performed 14% percentage compared to other network models with reduced prediction error and fast convergence rate. |
| first_indexed | 2025-11-15T19:52:25Z |
| format | Thesis |
| id | uthm-865 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-15T19:52:25Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-8652021-09-06T05:43:57Z http://eprints.uthm.edu.my/865/ A functional link neural network with modified cuckoo search for prediction tasks Abu Bakar, Siti Zulaikha QA76 Computer software QA71-90 Instruments and machines QA75-76.95 Calculating machines The impact of temperature, relative humidity and ozone changes bring a sharp warming climate. These changes can cause extreme consequences such as floods, hurricanes, heat waves and droughts. Therefore, prediction of temperature and relative humidity is an important factor to measure the environmental changes. Neural network, especially the Multi-Layer Perceptron (MLP) which uses Back Propagation algorithm (BP) as a supervised learning method, has been successfully applied in various problems for meteorological prediction tasks. However, this architecture has still been facing problems which the convergence rate is very low due to the multi layering topology of the network. Thus, this research proposed an implementation of Functional Link Neural Network (FLNN) which composed of a single layer of tunable weight trained with the Modified Cuckoo Search algorithm (MCS). The proposed approach was used to predict the daily temperatures, relative humidity and ozone data. Extensive simulation results have been compared with standard MLP trained with the BP, FLNN with BP and FLNN with CS. Promising results have shown that the proposed model has successfully out performed 14% percentage compared to other network models with reduced prediction error and fast convergence rate. 2017-08 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/865/1/24p%20SITI%20ZULAIKHA%20ABU%20BAKAR.pdf text en http://eprints.uthm.edu.my/865/2/SITI%20ZULAIKHA%20ABU%20BAKAR%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/865/3/SITI%20ZULAIKHA%20ABU%20BAKAR%20WATERMARK.pdf Abu Bakar, Siti Zulaikha (2017) A functional link neural network with modified cuckoo search for prediction tasks. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | QA76 Computer software QA71-90 Instruments and machines QA75-76.95 Calculating machines Abu Bakar, Siti Zulaikha A functional link neural network with modified cuckoo search for prediction tasks |
| title | A functional link neural network with modified cuckoo search for prediction tasks |
| title_full | A functional link neural network with modified cuckoo search for prediction tasks |
| title_fullStr | A functional link neural network with modified cuckoo search for prediction tasks |
| title_full_unstemmed | A functional link neural network with modified cuckoo search for prediction tasks |
| title_short | A functional link neural network with modified cuckoo search for prediction tasks |
| title_sort | functional link neural network with modified cuckoo search for prediction tasks |
| topic | QA76 Computer software QA71-90 Instruments and machines QA75-76.95 Calculating machines |
| url | http://eprints.uthm.edu.my/865/ http://eprints.uthm.edu.my/865/1/24p%20SITI%20ZULAIKHA%20ABU%20BAKAR.pdf http://eprints.uthm.edu.my/865/2/SITI%20ZULAIKHA%20ABU%20BAKAR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/865/3/SITI%20ZULAIKHA%20ABU%20BAKAR%20WATERMARK.pdf |