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Recurrent Neural Networks for Prediction
Danilo P. Mandic
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Description for Recurrent Neural Networks for Prediction
Hardcover. Neural networks consist of interconnected groups of neurons which function as processing units and aim to reconstruct the operation of the human brain. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 308 pages, Ill. BIC Classification: TJK; UYQN; UYS. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 250 x 175 x 23. Weight in Grams: 720.
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.
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 RNNsPresents on-line learning algorithms for nonlinear adaptive filters and ... Read more
- 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.
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Product Details
Format
Hardback
Publication date
2001
Publisher
John Wiley and Sons Ltd United Kingdom
Number of pages
308
Condition
New
Series
Adaptive and Learning Systems for Signal Processing, Communications and Control Series
Number of Pages
304
Place of Publication
New York, United States
ISBN
9780471495178
SKU
V9780471495178
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-50
About Danilo P. Mandic
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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