Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability 1st Edition by Danilo Mandic (PDF)

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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 nestingExamines stability and relaxation within RNNsPresents 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 normalisationStudies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iterationDescribes strategies for the exploitation of inherent relationships between parameters in RNNsDiscusses 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 processingRecurrent 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.VISIT OUR COMMUNICATIONS TECHNOLOGY WEBSITE! http://www.wiley.co.uk/commstech/VISIT OUR WEB PAGE! http://www.wiley.co.uk/

User’s Reviews

Editorial Reviews: From the Inside Flap 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 nestingExamines stability and relaxation within RNNsPresents 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 normalisationStudies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iterationDescribes strategies for the exploitation of inherent relationships between parameters in RNNsDiscusses 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 processingRecurrent 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. From the Back Cover 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 nestingExamines stability and relaxation within RNNsPresents 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 normalisationStudies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iterationDescribes strategies for the exploitation of inherent relationships between parameters in RNNsDiscusses 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 processingRecurrent 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. About the Author 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. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐”Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability,” approaches the field of recurrent neural networks from both a practical and a theoretical perspective. Starting from the fundamentals, where unexpected insights are offered even at the level of the dynamical richness of simple neurons, the authors describe many existing algorithms and gradually introduce novel ones. The latter are convicingly shown to yield better prediction performances than traditional approaches, when applied to real-world data. They also dedicate a considerable amount of time on the (practical) issue of nonlinearity analysis of time series, which is or should be, indeed, the cradle of all proper modelling and/or filtering solutions: nonlinearity should be assessed prior to choosing the appropriate model and/or filters, since linear ones are to be preferred if sufficient for the problem. I would recommend this book to any researcher who is active in the field of recurrent neural networks and time series analysis, but also to researchers who are new in the field, since the book offers an extensive overview of the current state-of-the-art approaches.

⭐I give this book 5 stars. It is a must have book, very well written. It has good balance between rigorous theory and authors reasonong regarding the subject. I’m not a beginner in this field, and still I found a lot of interesting ideas, that can help not only to improve quality of the net, but also make you see “bigger picture”.

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