The Experts below are selected from a list of 75 Experts worldwide ranked by ideXlab platform
Andreas Schober - One of the best experts on this subject based on the ideXlab platform.
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a modified general Regression neural network mgrnn with new efficient training algorithms as a robust black box tool for data analysis
Neural Networks, 2001Co-Authors: Dirk Tomandl, Andreas SchoberAbstract:A Modified General Regression Neural Network (MGRNN) is presented as an easy-to-use 'black box'-tool to feed in available data and obtain a reasonable Regression Surface. The MGRNN is based on the General Regression Neural Network by D. Specht [Specht, D. (1991). A General Regression Neural Network. IEEE Transactions on Neural Networks, 2(6), 568-576], therefore, the network's architecture and weights are determined. The kernel width of each training sample is trained by two supervised training algorithms. These fast and reliable algorithms require four user-definable parameters, but are robust against changes of the parameters. Its generalization ability was tested with different benchmarks: intertwined spirals, Mackey--Glass time series and PROBEN1. The MGRNN provides two additional features: (1) it is trainable with arbitrary data as long as a suitable metric exists. Particularly, it is unnecessary to force the data structure to vectors of equal length; (2) it is able to compute the gradient of the Regression Surface as long as the gradient of the metric is definable and defined. The MGRNN solves common practical problems of common feed-forward networks.
Dirk Tomandl - One of the best experts on this subject based on the ideXlab platform.
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a modified general Regression neural network mgrnn with new efficient training algorithms as a robust black box tool for data analysis
Neural Networks, 2001Co-Authors: Dirk Tomandl, Andreas SchoberAbstract:A Modified General Regression Neural Network (MGRNN) is presented as an easy-to-use 'black box'-tool to feed in available data and obtain a reasonable Regression Surface. The MGRNN is based on the General Regression Neural Network by D. Specht [Specht, D. (1991). A General Regression Neural Network. IEEE Transactions on Neural Networks, 2(6), 568-576], therefore, the network's architecture and weights are determined. The kernel width of each training sample is trained by two supervised training algorithms. These fast and reliable algorithms require four user-definable parameters, but are robust against changes of the parameters. Its generalization ability was tested with different benchmarks: intertwined spirals, Mackey--Glass time series and PROBEN1. The MGRNN provides two additional features: (1) it is trainable with arbitrary data as long as a suitable metric exists. Particularly, it is unnecessary to force the data structure to vectors of equal length; (2) it is able to compute the gradient of the Regression Surface as long as the gradient of the metric is definable and defined. The MGRNN solves common practical problems of common feed-forward networks.
Chonghun Han - One of the best experts on this subject based on the ideXlab platform.
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a nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns
Computers & Chemical Engineering, 2000Co-Authors: Sungyong Park, Chonghun HanAbstract:Abstract An accurate on-line measurement of quality variables are essential for the successful monitoring and control tasks in chemical process operations. However, due to the measurement difficulties such as the large time delays, the soft sensor, an inferential model, for the target quality variable, has been widely used as an alternative for the physical sensors. Partial least-squares (PLS) was used to develop a soft sensor because it can handle the correlations among many variables. However, the successful applications of linear projection methods like PLS were limited to only the cases without strong nonlinearities. This paper proposes a design methodology to build a soft sensor for chemical processes that can handle the correlations among many process variables and nonlinearities based on smoothness concept. The method has been directly motivated by the locally weighted Regression that estimates a Regression Surface through multivariate smoothing. The proposed method will be illustrated by comparisons with other familiar methods. The industrial case studies have shown that the proposed method gives a better or equal performances over other methods such as PLS, nonlinear PLS and artificial neural networks.
Hansgeorg Muller - One of the best experts on this subject based on the ideXlab platform.
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continuously additive models for nonlinear functional Regression
Biometrika, 2013Co-Authors: Hansgeorg Muller, Yichao WuAbstract:We introduce continuously additive models, which can be viewed as extensions of additive Regression models with vector predictors to the case of infinite-dimensional predictors. This approach produces a class of flexible functional nonlinear Regression models, where random predictor curves are coupled with scalar responses. In continuously additive modelling, integrals taken over a smooth Surface along graphs of predictor functions relate the predictors to the responses in a nonlinear fashion. We use tensor product basis expansions to fit the smooth Regression Surface that characterizes the model. In a theoretical investigation, we show that the predictions obtained from fitting continuously additive models are consistent and asymptotically normal. We also consider extensions to generalized responses. The proposed class of models outperforms existing functional Regression models in simulations and real-data examples. Copyright 2013, Oxford University Press.
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preaveraged localized orthogonal polynomial estimators for Surface smoothing and partial differentiation
Journal of the American Statistical Association, 1992Co-Authors: A S Azari, Hansgeorg MullerAbstract:Abstract We propose a multivariate smoothing method based on products of localized orthogonal polynomial series estimators for a smooth Regression Surface in the fixed-design Regression model. The estimation of partial derivatives is included. The proposed method provides for automatic and efficient boundary modifications near the edges of the Surface, assuming that the boundary of the support of the Regression function satisfies some regularity conditions. By allowing for a preaveraging step, the corresponding algorithms are speeded up considerably and are easy to implement. Computation of special boundary kernels, as required by the kernel method to avoid edge effects, is not necessary. It is shown that under sufficient smoothness assumptions, the global average mean squared error has the same optimal rate of convergence as the mean squared error at an interior point; that is, the boundary correction is asymptotically effective. The method depends on two smoothing parameters, one determining the amount ...
Houman Borouchaki - One of the best experts on this subject based on the ideXlab platform.
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a new edge detector based on parametric Surface model Regression Surface descriptor
arXiv: Image and Video Processing, 2019Co-Authors: Remi Cogranne, Remi Slysz, Laurence Moreau, Houman BorouchakiAbstract:In this paper we present a new methodology for edge detection in digital images. The first originality of the proposed method is to consider image content as a parametric Surface. Then, an original parametric local model of this Surface representing image content is proposed. The few parameters involved in the proposed model are shown to be very sensitive to discontinuities in Surface which correspond to edges in image content. This naturally leads to the design of an efficient edge detector. Moreover, a thorough analysis of the proposed model also allows us to explain how these parameters can be used to obtain edge descriptors such as orientations and curvatures. In practice, the proposed methodology offers two main advantages. First, it has high customization possibilities in order to be adjusted to a wide range of different problems, from coarse to fine scale edge detection. Second, it is very robust to blurring process and additive noise. Numerical results are presented to emphasis these properties and to confirm efficiency of the proposed method through a comparative study with other edge detectors.