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.
|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||International Joint Conference on Neural Networks (IJCNN'2000)|
|Period||00/7/24 → 00/7/27|
ASJC Scopus subject areas