BAT-BP: A new BAT based back-propagation algorithm for efficient data classification
Training neural networks particularly back propagation algorithm is a complex task of great importance in the field of supervised learning. One of the nature inspired meta-heuristic Bat algorithm is becoming a popular method in solving many complex optimization problems. Thus, this study investigate...
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| Format: | Article |
| Language: | en |
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Asian Research Publishing Network (ARPN)
2016
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| Online Access: | http://eprints.uthm.edu.my/4297/1/AJ%202016%20%2835%29.pdf http://eprints.uthm.edu.my/4297/ |
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| _version_ | 1833417562474414080 |
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| author | Mohd. Nawi, Nazri M. Z., Rehman Hafifi, Nurfarian Khan, Abdullah Siming, Insaf Ali |
| author_facet | Mohd. Nawi, Nazri M. Z., Rehman Hafifi, Nurfarian Khan, Abdullah Siming, Insaf Ali |
| author_sort | Mohd. Nawi, Nazri |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Training neural networks particularly back propagation algorithm is a complex task of great importance in the field of supervised learning. One of the nature inspired meta-heuristic Bat algorithm is becoming a popular method in solving many complex optimization problems. Thus, this study investigates the use of Bat algorithm along with back-propagation neural network (BPNN) algorithm in-order to gain optimal weights to solve the local minima problem and also to enhance the convergence rate. This study intends to show the superiority (time performance and quality of solution) of the proposed meta-heuristic Bat-BP algorithm over other more standard neural network training algorithms. The performance of the proposed Bat-BP algorithm is then compared with Artificial Bee Colony using BPNN (ABC-BP), Artificial Bee Colony using Levenberg-Marquardt (ABC-LM) and BPNN algorithm. Classification datasets from UCI machine learning repository are used to train the network. The simulation results show that the efficiency of BPNN training process is highly enhanced when combined with BAT algorithm. |
| format | Article |
| id | my.uthm.eprints-4297 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2016 |
| publisher | Asian Research Publishing Network (ARPN) |
| record_format | eprints |
| spelling | my.uthm.eprints-42972021-12-02T02:37:11Z http://eprints.uthm.edu.my/4297/ BAT-BP: A new BAT based back-propagation algorithm for efficient data classification Mohd. Nawi, Nazri M. Z., Rehman Hafifi, Nurfarian Khan, Abdullah Siming, Insaf Ali QA299.6-433 Analysis Training neural networks particularly back propagation algorithm is a complex task of great importance in the field of supervised learning. One of the nature inspired meta-heuristic Bat algorithm is becoming a popular method in solving many complex optimization problems. Thus, this study investigates the use of Bat algorithm along with back-propagation neural network (BPNN) algorithm in-order to gain optimal weights to solve the local minima problem and also to enhance the convergence rate. This study intends to show the superiority (time performance and quality of solution) of the proposed meta-heuristic Bat-BP algorithm over other more standard neural network training algorithms. The performance of the proposed Bat-BP algorithm is then compared with Artificial Bee Colony using BPNN (ABC-BP), Artificial Bee Colony using Levenberg-Marquardt (ABC-LM) and BPNN algorithm. Classification datasets from UCI machine learning repository are used to train the network. The simulation results show that the efficiency of BPNN training process is highly enhanced when combined with BAT algorithm. Asian Research Publishing Network (ARPN) 2016 Article PeerReviewed text en http://eprints.uthm.edu.my/4297/1/AJ%202016%20%2835%29.pdf Mohd. Nawi, Nazri and M. Z., Rehman and Hafifi, Nurfarian and Khan, Abdullah and Siming, Insaf Ali (2016) BAT-BP: A new BAT based back-propagation algorithm for efficient data classification. ARPN Journal of Engineering and Applied Sciences, 11 (24). pp. 14048-14051. ISSN 1819-6608 |
| spellingShingle | QA299.6-433 Analysis Mohd. Nawi, Nazri M. Z., Rehman Hafifi, Nurfarian Khan, Abdullah Siming, Insaf Ali BAT-BP: A new BAT based back-propagation algorithm for efficient data classification |
| title | BAT-BP: A new BAT based back-propagation algorithm for efficient data classification |
| title_full | BAT-BP: A new BAT based back-propagation algorithm for efficient data classification |
| title_fullStr | BAT-BP: A new BAT based back-propagation algorithm for efficient data classification |
| title_full_unstemmed | BAT-BP: A new BAT based back-propagation algorithm for efficient data classification |
| title_short | BAT-BP: A new BAT based back-propagation algorithm for efficient data classification |
| title_sort | bat-bp: a new bat based back-propagation algorithm for efficient data classification |
| topic | QA299.6-433 Analysis |
| url | http://eprints.uthm.edu.my/4297/1/AJ%202016%20%2835%29.pdf http://eprints.uthm.edu.my/4297/ |
| url_provider | http://eprints.uthm.edu.my/ |