Constructing stochastic networks via β-RBF networks

Sheng-Tun Li, Ernst L. Leiss

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

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 languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE
Pages19-24
Number of pages6
Volume1
Publication statusPublished - 1996
EventProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA
Duration: 1996 Jun 31996 Jun 6

Other

OtherProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
CityWashington, DC, USA
Period96-06-0396-06-06

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Radial basis function networks
Neural networks

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Li, S-T., & Leiss, E. L. (1996). Constructing stochastic networks via β-RBF networks. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 1, pp. 19-24). IEEE.
Li, Sheng-Tun ; Leiss, Ernst L. / Constructing stochastic networks via β-RBF networks. IEEE International Conference on Neural Networks - Conference Proceedings. Vol. 1 IEEE, 1996. pp. 19-24
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title = "Constructing stochastic networks via β-RBF networks",
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.",
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Li, S-T & Leiss, EL 1996, Constructing stochastic networks via β-RBF networks. in IEEE International Conference on Neural Networks - Conference Proceedings. vol. 1, IEEE, pp. 19-24, Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4), Washington, DC, USA, 96-06-03.

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 proceedingConference contribution

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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.

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Li S-T, Leiss EL. Constructing stochastic networks via β-RBF networks. In IEEE International Conference on Neural Networks - Conference Proceedings. Vol. 1. IEEE. 1996. p. 19-24