Abstract
In this paper, Universal Learning Networks with Branch Control of Relative Strength (ULNs with BR) is studied, which consists of basic networks and branch control networks. The branch control network can be used to determine which intermediate nodes of the basic network should be connected to the output node with a coefficient of relative strength ranging from zero to one. This determination will adjust the outputs of the intermediate nodes of the basic network. Therefore, by using ULNs with BC, locally functions distributed networks can be realized depending on the values of the inputs of the network. ULNs with BR is applied to a two-spirals problem. The simulation results show that ULNs with BC exhibits better performance than the conventional networks with comparable complexity.
| Original language | English |
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| Pages | 2361-2367 |
| Number of pages | 7 |
| Publication status | Published - 2001 |
| Externally published | Yes |
| Event | International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States Duration: 2001 Jul 15 → 2001 Jul 19 |
Conference
| Conference | International Joint Conference on Neural Networks (IJCNN'01) |
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| Country/Territory | United States |
| City | Washington, DC |
| Period | 01/7/15 → 01/7/19 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence