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1998 IEEE Symposium on Advances in Digital Filtering and Signal Processing. Symposium Proceedings (Cat. No.98EX185), 2002
To obtain an accurate model of a process the adaptation process should allow for an arbitrary accuracy within a given cost. Cost may be measured in terms of processing time or computing requirements. It is well known that to gain a better approximation of a process, the adaptation should be able to model a non-linearity at a desirable precision ...
A. David, T. Aboulnasr
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To obtain an accurate model of a process the adaptation process should allow for an arbitrary accuracy within a given cost. Cost may be measured in terms of processing time or computing requirements. It is well known that to gain a better approximation of a process, the adaptation should be able to model a non-linearity at a desirable precision ...
A. David, T. Aboulnasr
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Theoretical and Applied Genetics, 1982
A model, developed by Seyffert and Forkmann (1976), simulates quantitative characters by genes with biochemically definable action. This model, however, possesses a number of shortcomings which have been overcome by a modified model of the form: [Formula: see text] where [Formula: see text] is the score of the genotype [x1,...
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A model, developed by Seyffert and Forkmann (1976), simulates quantitative characters by genes with biochemically definable action. This model, however, possesses a number of shortcomings which have been overcome by a modified model of the form: [Formula: see text] where [Formula: see text] is the score of the genotype [x1,...
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(Non) linear regression modeling [PDF]
, 2004 We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1,…,Yl), l ∈ N, which are explained by a model, and independent (exogenous, explanatory) variables X = (X1,…,Xp),p ∈ N, which explain or predict the dependent variables by means of the model. Such
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17th European Microwave Conference, 1987, 1987
A new technique for non-linear modelling of MESFETs has been developed based on the extraction of both linear and non-linear model parameters in one optimization step. The technique eliminates various modelling inconsistency problems and can be used for MESFETs with dispersi ve model parameters.
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A new technique for non-linear modelling of MESFETs has been developed based on the extraction of both linear and non-linear model parameters in one optimization step. The technique eliminates various modelling inconsistency problems and can be used for MESFETs with dispersi ve model parameters.
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1994
In linear regression the mean surface in sample space is a plane. In non-linear regression the mean surface may be an arbitrary curved surface but in other respects the models are similar. In practice the mean surface in most non-linear regression models will be approximately planar in the region(s) of high likelihood allowing good approximations based
W. N. Venables, B. D. Ripley
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In linear regression the mean surface in sample space is a plane. In non-linear regression the mean surface may be an arbitrary curved surface but in other respects the models are similar. In practice the mean surface in most non-linear regression models will be approximately planar in the region(s) of high likelihood allowing good approximations based
W. N. Venables, B. D. Ripley
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Exploration Geophysics, 1977
A non-linear optimisation procedure has been applied to the interpretation of two-dimensional gravity anomalies. In comparison with linear methods, the non-linear approach is more difficult to control but can give more realistic solutions. Successful application of the non-linear approach depends upon the selection of a reasonable starting model from ...
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A non-linear optimisation procedure has been applied to the interpretation of two-dimensional gravity anomalies. In comparison with linear methods, the non-linear approach is more difficult to control but can give more realistic solutions. Successful application of the non-linear approach depends upon the selection of a reasonable starting model from ...
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Bayesian Models for Non‐linear Autoregressions
Journal of Time Series Analysis, 1997We discuss classes of Bayesian mixture models for nonlinear autoregressive times series, based on developments in semiparametric Bayesian density estimation in recent years. The development involves formal classes of multivariate discrete mixture distributions, providing flexibility in modeling arbitrary nonlinearities in time series structure and a ...
Müller, Peter +2 more
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1989
In previous chapters we have encountered several models which depend on parameters that introduce parameter non-linearities into otherwise standard DLMs. Although the full class of DLMs provides an enormous variety of useful models, it is the case that, sometimes, elaborations to include models with unknown parameters result in such non-linearities ...
Mike West, Jeff Harrison
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In previous chapters we have encountered several models which depend on parameters that introduce parameter non-linearities into otherwise standard DLMs. Although the full class of DLMs provides an enormous variety of useful models, it is the case that, sometimes, elaborations to include models with unknown parameters result in such non-linearities ...
Mike West, Jeff Harrison
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ACE: A non-linear regression model
Chemometrics and Intelligent Laboratory Systems, 1988Abstract Frank, I.E. and Lanteri, S., 1988. ACE: a non-linear regressional model. Chemometrics and Intelligent Laboratory Systems , 3: 301–313. A non-linear regression model is discussed and applied to various structure—activity relationship problems.
I. E. FRANK, LANTERI, SILVIA
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