Nonlinear Modeling: Advanced Black-Box Techniques

✍ Scribed by Jonas Sjöberg, Lester S. H. Ngia (auth.), Johan A. K. Suykens, Joos Vandewalle (eds.)

• Publisher: Springer US
• Year: 1998
• Tongue: English
• Leaves: 264
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xvii
Neural Nets and Related Model Structures for Nonlinear System Identification....Pages 1-28
Enhanced Multi-Stream Kalman Filter Training for Recurrent Networks....Pages 29-53
The Support Vector Method of Function Estimation....Pages 55-85
Parametric Density Estimation for the Classification of Acoustic Feature Vectors in Speech Recognition....Pages 87-118
Wavelet Based Modeling of Nonlinear Systems....Pages 119-148
Nonlinear Identification Based on Fuzzy Models....Pages 149-175
Statistical Learning in Control and Matrix Theory....Pages 177-207
Nonlinear Time-Series Analysis....Pages 209-239
The K.U.Leuven Time Series Prediction Competition....Pages 241-253
Back Matter....Pages 255-256

✦ Subjects

Circuits and Systems;Statistical Physics, Dynamical Systems and Complexity;Systems Theory, Control;Signal, Image and Speech Processing;Electrical Engineering