Characteristics of small scale non-monotonic neuron networks having large potentiality for learning

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 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 languageEnglish
Pages171-174
Number of pages4
Publication statusPublished - 2000 Jan 1
EventInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
Duration: 2000 Jul 242000 Jul 27

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy
Period00/7/2400/7/27

ASJC Scopus subject areas

  • Software

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