Optimisation of Self Organising Maps Using the Bat Algorithm
| dc.contributor.author | Mzelikahle, Kernan | |
| dc.contributor.author | Mapuma, Dunstan Junior | |
| dc.contributor.author | Hlatywayo, Dumisani J. | |
| dc.contributor.author | Trimble, John | |
| dc.contributor.author | Hlatywayo, Dumisani J. | |
| dc.date.accessioned | 2018-04-09T14:30:33Z | |
| dc.date.accessioned | 2023-06-26T13:07:15Z | |
| dc.date.available | 2018-04-09T14:30:33Z | |
| dc.date.available | 2023-06-26T13:07:15Z | |
| dc.date.issued | 2017 | |
| dc.description | Journal Article. | en_US |
| dc.description.abstract | Self Organising Maps are among the most widely used unsupervised neural network approaches to clustering. They have been shown to be efficient in handling large and high dimensional data. The Bat Algorithm is a swarm intelligence based, meta- heuristic optimisation algorithm. It is based on the echolocation behaviour of micro-bats with varying emission pulse rates and loudness. This paper gives a novel hybrid optimisation method which is here called the Bat Optimised Self-Organising Map. It combines the basic Self Organising Map learning algorithm with the Bat Algorithm. In this optimisation technique, the Bat Algorithm is used to initialise the weight vectors for a Self Organising Map to a near global optimum prior to the competition. | en_US |
| dc.identifier.citation | Mzelikahle, K. et al. 2017.Optimisation of Self Organising Maps Using the Bat Algorithm. American Journal of Information Science and Computer Engineering 3(6). | en_US |
| dc.identifier.issn | 2381-7496 | |
| dc.identifier.uri | http://196.220.97.103:4000/handle/123456789/885 | |
| dc.language.iso | en | en_US |
| dc.subject | Self Organising Maps | en_US |
| dc.subject | Bat Algorithm | en_US |
| dc.subject | Artificial Neural Networks | en_US |
| dc.subject | Unsupervised Learning | en_US |
| dc.title | Optimisation of Self Organising Maps Using the Bat Algorithm | en_US |
| dc.type | Article | en_US |