Isbn: 9780471349112 - nonlinear dynamical systems: feedforward neural network perspectives (adaptive and cognitive dynamic systems: signal processing, learning, communications and control) (23 Ergebnisse)

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Zustand: New. Examining a specialised part of neural networks, with applications in control, signal processing and time series analysis, this title provides an up-to-date treatment of a class of nonlinear dynamical systems using feed forward neural network structures. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 312 pages, Illustrations. BIC Classification: TJFM; UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 235 x 166 x 25. Weight in Grams: 604. . 2001. 1st Edition. Hardcover. . . . .…

Zustand: New. IRWIN W. SANDBERG is a chaired professor at the University of Texas at Austin.JAMES T. LO teaches in the Department of Mathematics and Statistics, University of Maryland.CRAIG L. FANCOURT is a member of the Adaptive Image and Signal Processing Group at the .

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Hardcover. Zustand: Brand New. 1st edition. 312 pages. 9.50x6.50x0.75 inches. In Stock.

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Zustand: New. Examining a specialised part of neural networks, with applications in control, signal processing and time series analysis, this title provides an up-to-date treatment of a class of nonlinear dynamical systems using feed forward neural network structures. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 312 pages, Illustrations. BIC Classification: TJFM; UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 235 x 166 x 25. Weight in Grams: 604. . 2001. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.…

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Hardcover. Zustand: new. Hardcover. The first truly up-to-date look at the theory and capabilities of nonlinear dynamical systems that take the form of feedforward neural network structures Considered one of the most important types of structures in the study of neural networks and neural-like networks, feedforward networks incorporating dynamical elements have important properties and are of use in many applications. Specializing in experiential knowledge, a neural network stores and expands its knowledge base via strikingly human routes-through a learning process and information storage involving interconnection strengths known as synaptic weights. In Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives, six leading authorities describe recent contributions to the development of an analytical basis for the understanding and use of nonlinear dynamical systems of the feedforward type, especially in the areas of control, signal processing, and time series analysis. Moving from an introductory discussion of the different aspects of feedforward neural networks, the book then addresses: * Classification problems and the related problem of approximating dynamic nonlinear input-output maps * The development of robust controllers and filters * The capability of neural networks to approximate functions and dynamic systems with respect to risk-sensitive error * Segmenting a time series It then sheds light on the application of feedforward neural networks to speech processing, summarizing speech-related techniques, and reviewing feedforward neural networks from the viewpoint of fundamental design issues. An up-to-date and authoritative look at the ever-widening technical boundaries and influence of neural networks in dynamical systems, this volume is an indispensable resource for researchers in neural networks and a reference staple for libraries. This book deals with a specialized part of neural networks having applications in control, signal processing and time series analysis. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Zustand: Brand New. 1st edition. 312 pages. 9.50x6.50x0.75 inches. In Stock. This item is printed on demand.