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.
|出版ステータス||Published - 2001 1月 1|
|イベント||International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States|
継続期間: 2001 7月 15 → 2001 7月 19
|Conference||International Joint Conference on Neural Networks (IJCNN'01)|
|Period||01/7/15 → 01/7/19|
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