Nonlinear Modeling: Advanced Black-Box Techniques [Paperback]

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.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.Preface. 1. Neural Nets and Related Moló¾