抄録
We present a novel growing RBF network structure using SOM in this paper. It consists of SOM and RBF networks respectively. The SOM performs unsupervised learning and also the weight vectors belonging to its output nodes are transmitted to the hidden nodes in the RBF networks as the centers of RBF activation functions. as a result one to one correspondence relationship is realized between the output nodes in SOM and the hidden nodes in RBF networks. The RBF networks perform supervised training using delta rule. Therefore, the current output errors in the RBF networks can be used to determine where to insert a new SOM unit according to the rule. This also makes it possible to make the RBF networks grow until a performance criterion is fulfilled or until a desired network size is obtained. The simulations on the two-spirals benchmark are shown to prove the proposed networks have good performance.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | Proceedings - IEEE International Workshop on Robot and Human Interactive Communication |
| ページ | 107-109 |
| ページ数 | 3 |
| DOI | |
| 出版物ステータス | Published - 2000 |
| 外部発表 | Yes |
| イベント | 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000 - Osaka 継続期間: 2000 9 27 → 2000 9 29 |
Other
| Other | 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000 |
|---|---|
| 市 | Osaka |
| 期間 | 00/9/27 → 00/9/29 |
Fingerprint
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Human-Computer Interaction
これを引用
Growing RBF structures using self-organizing maps. / Xiong, Qingyu; Hirasawa, Kotaro; Furuzuki, Takayuki; Murata, Junichi.
Proceedings - IEEE International Workshop on Robot and Human Interactive Communication. 2000. p. 107-109 892479.研究成果: Conference contribution
}
TY - GEN
T1 - Growing RBF structures using self-organizing maps
AU - Xiong, Qingyu
AU - Hirasawa, Kotaro
AU - Furuzuki, Takayuki
AU - Murata, Junichi
PY - 2000
Y1 - 2000
N2 - We present a novel growing RBF network structure using SOM in this paper. It consists of SOM and RBF networks respectively. The SOM performs unsupervised learning and also the weight vectors belonging to its output nodes are transmitted to the hidden nodes in the RBF networks as the centers of RBF activation functions. as a result one to one correspondence relationship is realized between the output nodes in SOM and the hidden nodes in RBF networks. The RBF networks perform supervised training using delta rule. Therefore, the current output errors in the RBF networks can be used to determine where to insert a new SOM unit according to the rule. This also makes it possible to make the RBF networks grow until a performance criterion is fulfilled or until a desired network size is obtained. The simulations on the two-spirals benchmark are shown to prove the proposed networks have good performance.
AB - We present a novel growing RBF network structure using SOM in this paper. It consists of SOM and RBF networks respectively. The SOM performs unsupervised learning and also the weight vectors belonging to its output nodes are transmitted to the hidden nodes in the RBF networks as the centers of RBF activation functions. as a result one to one correspondence relationship is realized between the output nodes in SOM and the hidden nodes in RBF networks. The RBF networks perform supervised training using delta rule. Therefore, the current output errors in the RBF networks can be used to determine where to insert a new SOM unit according to the rule. This also makes it possible to make the RBF networks grow until a performance criterion is fulfilled or until a desired network size is obtained. The simulations on the two-spirals benchmark are shown to prove the proposed networks have good performance.
UR - http://www.scopus.com/inward/record.url?scp=33646675164&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646675164&partnerID=8YFLogxK
U2 - 10.1109/ROMAN.2000.892479
DO - 10.1109/ROMAN.2000.892479
M3 - Conference contribution
SN - 078036273X
SN - 9780780362734
SP - 107
EP - 109
BT - Proceedings - IEEE International Workshop on Robot and Human Interactive Communication
ER -