ISBN: 9780470066355 - complex valued nonlinear adaptive filters: noncircularity, widely linear and neural models: 61 (adaptive and cognitive dynamic systems: signal processing, learning, communications and control) (22 resultados)

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Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,800grams, ISBN:9780470066355. …

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,800grams, ISBN:9780470066355. …

Condición: New. pp. xviii + 324 Illus.

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Condición: New. The filtering of real world signals requires an adaptive mode of operation to deal with the statistically nonstationary nature of the data. Feedback and nonlinearity within filtering architectures are needed to cater for long time dependencies and possibly nonlinear signal generating mechanisms. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control. Num Pages: 344 pages, Illustrations. BIC Classification: TJK; UYS. Category: (P) Professional & Vocational. Dimension: 256 x 175 x 24. Weight in Grams: 754. . 2009. 1st Edition. Hardcover. . . . .…

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Condición: New. pp. xviii + 324 1st Edition.

Hardcover. Condición: Brand New. 1st edition. 344 pages. 9.75x6.75x0.75 inches. In Stock.

Gebunden. Condición: New. The filtering of real world signals requires an adaptive mode of operation to deal with the statistically nonstationary nature of the data. Feedback and nonlinearity within filtering architectures are needed to cater for long time dependencies and possibly .

Condición: New. The filtering of real world signals requires an adaptive mode of operation to deal with the statistically nonstationary nature of the data. Feedback and nonlinearity within filtering architectures are needed to cater for long time dependencies and possibly nonlinear signal generating mechanisms. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control. Num Pages: 344 pages, Illustrations. BIC Classification: TJK; UYS. Category: (P) Professional & Vocational. Dimension: 256 x 175 x 24. Weight in Grams: 754. . 2009. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.…

Buch. Condición: Neu. Neuware - This book examines nonlinear adaptive filtering in the complex domain, providing theoretical information and computational principles for optimizing applications in a range of fields. It begins with a full introduction to the topic, including background theory on standard complex statistics. The authors then go on to discuss the theoretical principles of complex valued nonlinear adaptive filters, and the concept of nonlinearity in general, before presenting learning algorithms for recurrent neural networks (RNN). The authors then use this fundamental information to cover more advanced topics such as nonlinear adaptive prediction and forecasting through simulation, and a statistical framework for detecting the nature of complex random variables. The final chapter sets out potential applications using these techniques in order to illustrate the benefit of this approach.…

Hardcover. Condición: new. Hardcover. This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals. The filtering of real world signals requires an adaptive mode of operation to deal with the statistically nonstationary nature of the data. Feedback and nonlinearity within filtering architectures are needed to cater for long time dependencies and possibly nonlinear signal generating mechanisms. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…