Name
Title | ||
|---|---|---|
Learning Rule Optimization and Comparative Evaluation of Accelerated Self-Organizing Maps for Industrial Applications |
Abstract | ||
|---|---|---|
The emergence of low latency and high bandwidth SG networks, alongside localized computation and data storage of edge computing are enabling real-time applications in industrial settings, such as smart grid, smart cities, and smart factories. The resolution, frequency and variety of data streams generated by such applications are not effectively processed and analysed by contemporary machine learning algorithms. This challenge is further complicated by the unlabelled and non-deterministic nature of the data streams. Hardware accelerated machine learning has been proposed to address some of these challenges but limited work has been published on unsupervised learning from unlabelled data. In this paper, we extend the hardware accelerated Self Organizing Map (SOM) algorithm by optimizing the learning rule for computational efficiency, followed by a comparative empirical evaluation with two other variants, tri-state SOM and integer SOM. We have used two datasets representative of real-time industrial applications in SG networks and smart grids, for this evaluation. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IECON48115.2021.9589053 | IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY |
DocType | ISSN | Citations |
Conference | 1553-572X | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Madhavi Gayathri | 1 | 0 | 0.34 |
| Amanda Ariyaratne | 2 | 0 | 0.34 |
| Sachin Kahawala | 3 | 0 | 0.34 |
| Daswin De Silva | 4 | 0 | 0.34 |
| Damminda Alahakoon | 5 | 0 | 0.34 |
| Vishaka Nanayakkara | 6 | 0 | 0.34 |
| Evgeny Osipov | 7 | 0 | 0.34 |
| Xinghuo Yu | 8 | 3 | 2.74 |