Recurrent Neural Networks for Prediction Tapa dura - 2001 - 1st Edición

Name: Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability
Price: 47.08 GBP
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Author: Danilo P. Mandic, Jonathon A. Chambers
ISBN: 9780471495178

de Mandic

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Información de la editorial

Durch die Anwendung rckbezglicher neuronaler Netze l t sich die Leistungsfhigkeit konventioneller Technologien der digitalen Datenverarbeitung signifikant erhhen. Von besonderer Bedeutung ist dies fr komplexe Aufgaben, wie z.B. die mobile Kommunikation, die Robotik und die Medizintechnik. Das Buch fa t Originalarbeiten zur Stabilitt neuronaler Netze zusammen und verbindet streng mathematische Analysen mit anschaulichen Anwendungen und experimentellen Belegen.

Primera línea

Artificial neural network (ANN) models have been extensively studied with the aim of achieving human-like performance, especially in the field of pattern recognition.

Descripción de contraportada

New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.

Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal architectures together with the concepts of modularity and nesting
Examines stability and relaxation within RNNs
Presents on-line learning algorithms for nonlinear adaptive filters and introduces new paradigms which exploit the concepts of a priori and a posteriori errors, data-reusing adaptation, and normalisation
Studies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iteration
Describes strategies for the exploitation of inherent relationships between parameters in RNNs
Discusses practical issues such as predictability and nonlinearity detecting and includes several practical applications in areas such as air pollutant modelling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing

Recurrent Neural Networks for Prediction offers a new insight into the learning algorithms, architectures and stability of recurrent neural networks and, consequently, will have instant appeal. It provides an extensive background for researchers, academics and postgraduates enabling them to apply such networks in new applications.

Detalles

Título Recurrent Neural Networks for Prediction
Autor Mandic
Encuadernación Tapa dura
Número de edición 1st
Edición 1
Páginas 304
Volúmenes 1
Idioma ENG
Editorial John Wiley & Sons
Fecha de publicación 2001-09-05
ISBN 9780471495178 / 0471495174
Peso 1.24 libras (0.56 kg)
Dimensiones 9.77 x 6.06 x 0.87 pulgadas (24.82 x 15.39 x 2.21 cm)
Library of Congress subjects Neural networks (Computer science), Machine learning
Número de catálogo de la Librería del Congreso de EEUU 2001033418
Dewey Decimal Code 006.32

Acerca del autor

Danilo Mandic from the Imperial College London, London, UK was named Fellow of the Institute of Electrical and Electronics Engineers in 2013 for contributions to multivariate and nonlinear learning systems.

Jonathon A. Chambers is the author of Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, published by Wiley.

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Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability

de Danilo P. Mandic, Jonathon A. Chambers

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Estado

Used - Good

ISBN 10 / ISBN 13

9780471495178 / 0471495174

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Waltham Abbey, Essex, United Kingdom

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