Study on DBM network with non-monotonic neurons

Mitsunaga Kinjo, Shigeo Sato, Koji Nakajima

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

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 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.

Original languageEnglish
Pages2347-2350
Number of pages4
Publication statusPublished - 1999 Dec 1
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 1999 Jul 101999 Jul 16

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period99/7/1099/7/16

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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