Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control
1 primary work • 2 total works
Book 59
Complex Valued Nonlinear Adaptive Filters
by Danilo P. Mandic and Vanessa Su Lee Goh
Published 17 April 2009
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.
Recurrent Neural Networks for Prediction
by Danilo P. Mandic and Jonathon A. Chambers
Published 6 August 2001
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. VISIT OUR COMMUNICATIONS TECHNOLOGY WEBSITE! http://www.wiley.co.uk/commstech/ VISIT OUR WEB PAGE! http://www.wiley.co.uk/
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. VISIT OUR COMMUNICATIONS TECHNOLOGY WEBSITE! http://www.wiley.co.uk/commstech/ VISIT OUR WEB PAGE! http://www.wiley.co.uk/