Nonlinear Modeling : Advanced Black-Box Techniques

Johan A. K. Suykens

ISBN 10: 1461376114 / ISBN 13: 9781461376118

Editore: Springer, Springer, 2012

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Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9781461376118

Dati bibliografici

• Titolo: Nonlinear Modeling : Advanced Black-Box Techniques
• Editore: Springer, Springer
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 1461376114
• ISBN 13: 9781461376118
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 240 L x 160 A x 16 P (millimetri) 240 millimetri di larghezza per 160 millimetri di altezza per 16 millimetri di profondità
• Peso: 452 grammi

Descrizione articolo

Riassunto

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.

Contenuti

Preface. 1. Neural Nets and Related Model Structures for Nonlinear System Identification; J. Sjöberg, L.S.H. Ngia. 2. Enhanced Multi-Stream Kalman Filter Training for Recurrent Networks; L.A. Feldkamp, et al. 3. The Support Vector Method of Function Estimation; V. Vapnik. 4. Parametric Density Estimation for the Classification of Acoustic Feature Vectors in Speech Recognition; S. Basu, C.A. Micchelli. 5. Wavelet Based Modeling of Nonlinear Systems; Yi Yu, et al. 6. Nonlinear Identification Based on Fuzzy Models; V. Wertz, S. Yurkovich. 7. Statistical Learning in Control and Matrix Theory; M. Vidyasagar. 8. Nonlinear Time-Series Analysis; U. Parlitz. 9. The K.U. Leuven Time Series Prediction Competition; J.A.K. Suykens, J. Vandewalle. References. Index.