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IJON
2008
156views more  IJON 2008»
14 years 9 months ago
Structural identifiability of generalized constraint neural network models for nonlinear regression
Identifiability becomes an essential requirement for learning machines when the models contain physically interpretable parameters. This paper presents two approaches to examining...
Shuang-Hong Yang, Bao-Gang Hu, Paul-Henry Courn&eg...
BC
2008
72views more  BC 2008»
14 years 10 months ago
Spike-train spectra and network response functions for non-linear integrate-and-fire neurons
Reduced models have long been used as a tool for the analysis of the complex activity taking place in neurons and their coupled networks. Recent advanced in experimental and theore...
Magnus J. E. Richardson
ISNN
2007
Springer
15 years 3 months ago
Recurrent Fuzzy CMAC for Nonlinear System Modeling
Normal fuzzy CMAC neural network performs well because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires hu...
Floriberto Ortiz Rodriguez, Wen Yu, Marco A. Moren...
ICIP
1999
IEEE
15 years 2 months ago
Nonlinear Multigrid Optimization for Bayesian Diffusion Tomography
Optical diffusion tomography attempts to reconstruct an object cross section from measurements of scattered and attenuated light. While Bayesian approaches are well suited to this...
Jong Chul Ye, Charles A. Bouman, Rick P. Millane, ...
ESANN
2004
14 years 11 months ago
On fields of nonlinear regression models
Abstract. In the context of nonlinear regression, we consider the problem of explaining a variable y from a vector x of explanatory variables and from a vector t of conditionning v...
Bruno Pelletier, Robert Frouin