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Nonlinear Modeling

Advanced Black-Box Techniques

By: Johan A.K. Suykens (Editor), Joos P.L. Vandewalle (Editor)

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Hardcover | 30 June 1998

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Hardcover
280 Pages

Dimensions(cm)
23.39 x 15.6 x 1.75

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Nonlinear Modeling: Advanced Black-Box Techniques discusses methods on

Neural nets and related model structures for nonlinear system identification;
Enhanced multi-stream Kalman filter training for recurrent networks;
The support vector method of function estimation;
Parametric density estimation for the classification of acoustic feature vectors in speech recognition;
Wavelet-based modeling of nonlinear systems;
Nonlinear identification based on fuzzy models;
Statistical learning in control and matrix theory;
Nonlinear time-series analysis.

It also contains the results of the K.U. Leuven time series prediction competition, held within the framework of an international workshop at the K.U. Leuven, Belgium in July 1998.

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Preface
Contributing Authors
Neural Nets and Related Model Structures for Nonlinear System Identification	p. 1
Enhanced Multi-Stream Kalman Filter Training for Recurrent Networks	p. 29
The Support Vector Method of Function Estimation	p. 55
Parametric Density Estimation for the Classification of Acoustic Feature Vectors in Speech Recognition	p. 87
Wavelet Based Modeling of Nonlinear Systems	p. 119
Nonlinear Identification based on Fuzzy Models	p. 149
Statistical Learning in Control and Matrix Theory	p. 177
Nonlinear Time-Series Analysis	p. 209
The K. U. Leuven Time Series Prediction Competition	p. 241
References	p. 251
Index	p. 254
Table of Contents provided by Blackwell. All Rights Reserved.

Nonlinear Modeling

Advanced Black-Box Techniques

At a Glance

Hardcover

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