Overlapped multi-neural-network: A case study

Jinglu Hu, Kotaro Hirasawa

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

This paper presents a case study for the overlapped multi-neural-network (OMNN). An overlapped multi-neural-network, structurally, is the same as an ordinary feedforward neural network, but it is considered as one consisting of several subnets. All subnets have the same input-output units, but some different hidden units. Input-output spaces are partitioned into several parts, each of which corresponds to one subnet of OMNN. Numerical simulations show that such an OMNN has superior performance in that it has better presentation ability than an ordinary neural network and better generalization ability than a non-overlapped multi-neural-network.

Original languageEnglish
Pages120-125
Number of pages6
Publication statusPublished - 2000 Jan 1
Externally publishedYes
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
  • Artificial Intelligence

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  • Cite this

    Hu, J., & Hirasawa, K. (2000). Overlapped multi-neural-network: A case study. 120-125. Paper presented at International Joint Conference on Neural Networks (IJCNN'2000), Como, Italy, .