Abstract
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 nonlinear problems, such as the 2-Parity problem and the 4-Parity problem, show that the DBM network with non-monotonic neurons has higher learning ability compared to the network with monotonic neurons.
Original language | English |
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Pages | 171-174 |
Number of pages | 4 |
Publication status | Published - 2000 Jan 1 |
Event | International Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy Duration: 2000 Jul 24 → 2000 Jul 27 |
Other
Other | International Joint Conference on Neural Networks (IJCNN'2000) |
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City | Como, Italy |
Period | 00/7/24 → 00/7/27 |
ASJC Scopus subject areas
- Software