Overlapped multi-neural-network: A case study

Takayuki Furuzuki, Kotaro Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Place of PublicationPiscataway, NJ, United States
Number of pages6
Publication statusPublished - 2000
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
Duration: 2000 Jul 242000 Jul 27


OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy


ASJC Scopus subject areas

  • Software

Cite this

Furuzuki, T., & Hirasawa, K. (2000). Overlapped multi-neural-network: A case study. In Proceedings of the International Joint Conference on Neural Networks (Vol. 1, pp. 120-125). IEEE.