Recurrent neural networks for prediction : learning algorithms, architectures and stability

Author

• Mandic, Danilo P.

Additional Author(s)

• Chambers, Jonathon A.

Publisher

Chicester, Sussex: John Wiley & Sons Ltd, 2001

Language

English

ISBN

0471495174

Series

Wiley series in adaptive and learning systems for signal processing, communications, and control

Subject(s)

• MACHINE LEARNING
• NEURAL NETWORKS (COMPUTER SCIENCE)

Notes

• Appendix: p. 223 - 266

. Bibliography: p. 267-280. Index: p. 281-285

Abstract

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.

Physical Dimension

Number of Page(s)

xxi, 285 p.

Dimension

25 cm.

Other Desc.

ill.

Summary / Review / Table of Content

No summary / review / table of content available!

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#	Accession No.	Call Number	Location	Status
1.	03473/08	006.32 Man R	Library - 7th Floor	Available
2.	01970/08	006.32 Man R	Library - 7th Floor	Available