In this paper, we report a study on learning ability of a Deterministic Boltzmann Machine (DBM) with neurons which have a non-monotonic activation function. We use an end-cut-off-type function with a threshold parameter `θ' as the non-monotonic function. Numerical simulations of learning nonlinear problems, such as the XOR problem and the ADD problem, show that the DBM network with non-monotonic neurons has higher learning ability compared to the network with monotonic neurons, and that the non-monotonic neural network has a novel effects which adjust the number of neurons. We have designed an integrated circuit of the 2-3-1 DBM network. The use of the non-monotonic neurons make it possible to integrate a large scale neural network because of the simple circuit design.
|出版ステータス||Published - 1999 12 1|
|イベント||International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA|
継続期間: 1999 7 10 → 1999 7 16
|Other||International Joint Conference on Neural Networks (IJCNN'99)|
|City||Washington, DC, USA|
|Period||99/7/10 → 99/7/16|
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