Overlapped multi-neural-network: A case study

Takayuki Furuzuki, Kotaro Hirasawa

研究成果: Conference contribution

2 引用 (Scopus)

抄録

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.

元の言語English
ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
出版場所Piscataway, NJ, United States
出版者IEEE
ページ120-125
ページ数6
1
出版物ステータスPublished - 2000
外部発表Yes
イベントInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
継続期間: 2000 7 242000 7 27

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
Como, Italy
期間00/7/2400/7/27

Fingerprint

Neural networks
Feedforward neural networks
Computer simulation

ASJC Scopus subject areas

  • Software

これを引用

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

Overlapped multi-neural-network : A case study. / Furuzuki, Takayuki; Hirasawa, Kotaro.

Proceedings of the International Joint Conference on Neural Networks. 巻 1 Piscataway, NJ, United States : IEEE, 2000. p. 120-125.

研究成果: Conference contribution

Furuzuki, T & Hirasawa, K 2000, Overlapped multi-neural-network: A case study. : Proceedings of the International Joint Conference on Neural Networks. 巻. 1, IEEE, Piscataway, NJ, United States, pp. 120-125, International Joint Conference on Neural Networks (IJCNN'2000), Como, Italy, 00/7/24.
Furuzuki T, Hirasawa K. Overlapped multi-neural-network: A case study. : Proceedings of the International Joint Conference on Neural Networks. 巻 1. Piscataway, NJ, United States: IEEE. 2000. p. 120-125
Furuzuki, Takayuki ; Hirasawa, Kotaro. / Overlapped multi-neural-network : A case study. Proceedings of the International Joint Conference on Neural Networks. 巻 1 Piscataway, NJ, United States : IEEE, 2000. pp. 120-125
@inproceedings{57334bc57d49491189ea84f95223ec2a,
title = "Overlapped multi-neural-network: A case study",
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.",
author = "Takayuki Furuzuki and Kotaro Hirasawa",
year = "2000",
language = "English",
volume = "1",
pages = "120--125",
booktitle = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "IEEE",

}

TY - GEN

T1 - Overlapped multi-neural-network

T2 - A case study

AU - Furuzuki, Takayuki

AU - Hirasawa, Kotaro

PY - 2000

Y1 - 2000

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0033717739&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033717739&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0033717739

VL - 1

SP - 120

EP - 125

BT - Proceedings of the International Joint Conference on Neural Networks

PB - IEEE

CY - Piscataway, NJ, United States

ER -