9780471369981 - Kalman Filtering and Neural Networks (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control) von Haykin, Simon (10 Ergebnisse)

hardcover. Zustand: Good. Good text, no notable marking, some corner bumping to cover and page tips. US orders shipped via US Mail. International orders shipped via DHL. Additional postage may be required on oversize books and sets. NO prison orders.

Zustand: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Hardcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. C 1209: (2001) 9780471369981 Sprache: Englisch Gewicht in Gramm: 550.

Zustand: New. In.

Zustand: New. pp. xiii + 284 Illus.

Gebunden. Zustand: New. Although the traditional approach to the subject is usually linear, this book recognizes and deals with the fact that real problems are most often nonlinear. (SciTech Book News, Vol. 25, No. 4, December 2001)SIMON HAYKIN, PhD, is Professor of Electric.

Hardcover. Zustand: Brand New. 284 pages. 9.25x6.25x0.50 inches. In Stock.

Zustand: New. Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 304 pages, Ill. BIC Classification: TJFC; UYQN; UYS. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 245 x 166 x 23. Weight in Grams: 582. . 2001. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - State-of-the-art coverage of Kalman filter methods for the design of neural networksThis self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear.The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover:\* An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF)\* Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes\* The dual estimation problem\* Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm\* The unscented Kalman filterEach chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.; Die Kalman-Filterung ist ein wichtiges Spezialgebiet der Steuerungstechnik und Signalverarbeitung und die höchstentwickelte Methode für das Design neuronaler Netze. Der unkonventionelle, nichtlineare Ansatz trägt der Tatsache Rechnung, dass in der Praxis meist nichtlineare Probleme von Bedeutung sind. Besprochen werden wichtige Anwendungen, zum Beispiel aus der Steuerungstechnik und der Finanzmathematik.