Abstract
Nonlinear radial basis functions at the single layer hidden units are effective in generating complex nonlinear mapping and at the same time facilitate fast linear learning. In this work, we propose a model and an algorithm to arrive at a near optimum initial configuration very quickly. Thus the position of the hidden units in the input space and the connection weights from the hidden units to the output units, instead of arbitrarily, are optimally set. Simulations on this initial configuration are performed. Different parameters are further trained and their effects experimented.
| Original language | English |
|---|---|
| Title of host publication | 1993 IEEE International Conference on Neural Networks |
| Editors | Anon |
| Place of Publication | Piscataway, NJ, United States |
| Publisher | Publ by IEEE |
| Pages | 466-472 |
| Number of pages | 7 |
| ISBN (Print) | 0780312007 |
| Publication status | Published - 1993 |
| Event | 1993 IEEE International Conference on Neural Networks - San Francisco, CA, USA Duration: 1993 Mar 28 → 1993 Apr 1 |
Other
| Other | 1993 IEEE International Conference on Neural Networks |
|---|---|
| City | San Francisco, CA, USA |
| Period | 93/3/28 → 93/4/1 |
Fingerprint
ASJC Scopus subject areas
- Engineering(all)
Cite this
Quickly trained artificial neural network with single hidden layer Gaussian units. / Chakraborty, Goutam; Shiratori, Norio; Noguchi, Shoichi.
1993 IEEE International Conference on Neural Networks. ed. / Anon. Piscataway, NJ, United States : Publ by IEEE, 1993. p. 466-472.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Quickly trained artificial neural network with single hidden layer Gaussian units
AU - Chakraborty, Goutam
AU - Shiratori, Norio
AU - Noguchi, Shoichi
PY - 1993
Y1 - 1993
N2 - Nonlinear radial basis functions at the single layer hidden units are effective in generating complex nonlinear mapping and at the same time facilitate fast linear learning. In this work, we propose a model and an algorithm to arrive at a near optimum initial configuration very quickly. Thus the position of the hidden units in the input space and the connection weights from the hidden units to the output units, instead of arbitrarily, are optimally set. Simulations on this initial configuration are performed. Different parameters are further trained and their effects experimented.
AB - Nonlinear radial basis functions at the single layer hidden units are effective in generating complex nonlinear mapping and at the same time facilitate fast linear learning. In this work, we propose a model and an algorithm to arrive at a near optimum initial configuration very quickly. Thus the position of the hidden units in the input space and the connection weights from the hidden units to the output units, instead of arbitrarily, are optimally set. Simulations on this initial configuration are performed. Different parameters are further trained and their effects experimented.
UR - http://www.scopus.com/inward/record.url?scp=0027306610&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0027306610&partnerID=8YFLogxK
M3 - Conference contribution
SN - 0780312007
SP - 466
EP - 472
BT - 1993 IEEE International Conference on Neural Networks
A2 - Anon, null
PB - Publ by IEEE
CY - Piscataway, NJ, United States
ER -