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Nonlinear Modeling: Advanced Black-Box Techniques - Hardcover

 
9780792381952: Nonlinear Modeling: Advanced Black-Box Techniques

Inhaltsangabe

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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Über die Autorin bzw. den Autor

Jairo Espinosa had a considerable experience of the practitioner side of advanced control systems and fuzzy systems in particular working with such companies as Zenith Data Systems in his native Colombia. There, he also won prizes for his academic work and for electronic design. He now works for IPCOS a company specialising in the design of advanced control systems for many process industries. This wil allow the author to draw on a good selection of industrial situations in writing the book. Vincent Wertz is now head of the Automatic Control Group at Louvain where he is particularly active in Ph.D. supervision work (his contributions to the book will ensure relevance to the graduate market) and the book reflects all of his main research interests.

Von der hinteren Coverseite

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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