Abstract
Without considering spatial, stochastic, and temporal features inherent in natural neural systems, the computational power of conventional artificial neural networks (ANNs) is limited. In the present paper, we look at the stochastic complexity and construct a stochastic ANN by modeling stochastic fluctuations in the environmental stimuli such that all stimuli are prone to be corrupted by noise or even outliers and to break networks down; therefore, a positive-breakdown network is required. We investigate the stochasticity in the domain of function approximation (estimation) in the framework of radial basis function networks (RBFNs) and propose a robust RBFN, β-RBFN, by applying the breakdown point approach in robust regression. Experimental results demonstrate the advantages of the proposed networks in robustness and simplicity over the plain RBFNs.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Publisher | IEEE |
| Pages | 19-24 |
| Number of pages | 6 |
| Volume | 1 |
| Publication status | Published - 1996 |
| Event | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA Duration: 1996 Jun 3 → 1996 Jun 6 |
Other
| Other | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) |
|---|---|
| City | Washington, DC, USA |
| Period | 96-06-03 → 96-06-06 |
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All Science Journal Classification (ASJC) codes
- Software
Cite this
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Constructing stochastic networks via β-RBF networks. / Li, Sheng-Tun; Leiss, Ernst L.
IEEE International Conference on Neural Networks - Conference Proceedings. Vol. 1 IEEE, 1996. p. 19-24.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Constructing stochastic networks via β-RBF networks
AU - Li, Sheng-Tun
AU - Leiss, Ernst L.
PY - 1996
Y1 - 1996
N2 - Without considering spatial, stochastic, and temporal features inherent in natural neural systems, the computational power of conventional artificial neural networks (ANNs) is limited. In the present paper, we look at the stochastic complexity and construct a stochastic ANN by modeling stochastic fluctuations in the environmental stimuli such that all stimuli are prone to be corrupted by noise or even outliers and to break networks down; therefore, a positive-breakdown network is required. We investigate the stochasticity in the domain of function approximation (estimation) in the framework of radial basis function networks (RBFNs) and propose a robust RBFN, β-RBFN, by applying the breakdown point approach in robust regression. Experimental results demonstrate the advantages of the proposed networks in robustness and simplicity over the plain RBFNs.
AB - Without considering spatial, stochastic, and temporal features inherent in natural neural systems, the computational power of conventional artificial neural networks (ANNs) is limited. In the present paper, we look at the stochastic complexity and construct a stochastic ANN by modeling stochastic fluctuations in the environmental stimuli such that all stimuli are prone to be corrupted by noise or even outliers and to break networks down; therefore, a positive-breakdown network is required. We investigate the stochasticity in the domain of function approximation (estimation) in the framework of radial basis function networks (RBFNs) and propose a robust RBFN, β-RBFN, by applying the breakdown point approach in robust regression. Experimental results demonstrate the advantages of the proposed networks in robustness and simplicity over the plain RBFNs.
UR - http://www.scopus.com/inward/record.url?scp=0029749563&partnerID=8YFLogxK
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M3 - Conference contribution
VL - 1
SP - 19
EP - 24
BT - IEEE International Conference on Neural Networks - Conference Proceedings
PB - IEEE
ER -