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Other titles in the Adaptive and Learning Systems for Signal Processing, Communications and Control series:

Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models (Adaptive and Learning Systems for Signal Processing, Communications and Control)

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Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models (Adaptive and Learning Systems for Signal Processing, Communications and Control) Cover

 

Synopses & Reviews

Publisher Comments:

The filtering of real world signals requires an adaptive mode of operation to deal with the statistically nonstationary nature of the data. Feedback and nonlinearity within filtering architectures are needed to cater for long time dependencies and possibly nonlinear signal generating mechanisms. Using the authors’ original research and current established methods, this book covers the foundations of standard complex adaptive filtering and offers next generation solutions for the generality of complex valued signals. It provides a rigorous treatment of complex noncircularity and nonlinearity, thus avoiding the deficiencies inherent in several mathematical shortcuts typically used in the treatment of complex random signals. Simulations for both circular and noncircular data sources are included—from benchmark models to real world directional processes such as wind and radar signals.

Key features:

  • Provides theoretical and practical justification for converting many apparently real valued signal processing problems into the complex domain;
  • Offers a unified approach to the design of complex valued adaptive filters and temporal neural networks, based on augmented complex statistics and the duality between the bivariate and complex calculus (CR calculus);
  • Introduces augmented filtering algorithms based on widely linear models, making them suitable for processing both second order circular (proper) and noncircular (improper) complex signals;
  • Covers adaptive stepsizes, dynamical range reduction, validity of complex representations, and data driven time–frequency decompositions;
  • Includes extensive background material in appendices ranging from the theory of complex variables through to fixed point theory.

Complex valued signals play a central role in the fields of communications, radar, sonar, array, biomedical and environmental signal processing amongst others. This book will have wide appeal to researchers and practising engineers in these and related disciplines, and can also be used as lecture material for a course on adaptive filters.

Synopsis:

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.

Synopsis:

This book examines nonlinear adaptive filtering in the complex domain, providing theoretical information and computational principles for optimizing applications in a range of fields. It begins with a full introduction to the topic, including background theory on standard complex statistics. The authors then go on to discuss the theoretical principles of complex valued nonlinear adaptive filters, and the concept of nonlinearity in general, before presenting learning algorithms for recurrent neural networks (RNN). The authors then use this fundamental information to cover more advanced topics such as nonlinear adaptive prediction and forecasting through simulation, and a statistical framework for detecting the nature of complex random variables. The final chapter sets out potential applications using these techniques in order to illustrate the benefit of this approach.
  • Provides a rigorous theoretical foundation for efficient adaptive processing of complex signals, illustrating the benefit of this approach for a number of practical applications
  • Presents theoretical and computational principles for signal processing in the complex domain, which is essential for the optimization of a range of important applications in the fields of radar, sonar, communications, acoustics and GIS. 
  • Includes practical and comprehensive simulation examples, conducted on both benchmark and real-world data, such as wind and radar measurements. 
  • Sets out a statistical framework for detecting the nature of complex random variables. 

Table of Contents

Preface xiii

Acknowledgements xvii

1 The Magic of Complex Numbers 1

1.1 History of Complex Numbers 2

1.2 History of Mathematical Notation 8

1.3 Development of Complex Valued Adaptive Signal Processing 9

2 Why Signal Processing in the Complex Domain? 13

2.1 Some Examples of Complex Valued Signal Processing 13

2.2 Modelling in C is Not Only Convenient But Also Natural 19

2.3 Why Complex Modelling of Real Valued Processes? 20

2.4 Exploiting the Phase Information 23

2.5 Other Applications of Complex Domain Processing of Real Valued Signals 26

2.6 Additional Benefits of Complex Domain Processing 29

3 Adaptive Filtering Architectures 33

3.1 Linear and Nonlinear Stochastic Models 34

3.2 Linear and Nonlinear Adaptive Filtering Architectures 35

3.3 State Space Representation and Canonical Forms 39

4 Complex Nonlinear Activation Functions 43

4.1 Properties of Complex Functions 43

4.2 Universal Function Approximation 46

4.3 Nonlinear Activation Functions for Complex Neural Networks 48

4.4 Generalised Splitting Activation Functions (GSAF) 53

4.5 Summary: Choice of the Complex Activation Function 54

5 Elements of CR Calculus 55

5.1 Continuous Complex Functions 56

5.2 The Cauchy–Riemann Equations 56

5.3 Generalised Derivatives of Functions of Complex Variable 57

5.4 CR-derivatives of Cost Functions 62

6 Complex Valued Adaptive Filters 69

6.1 Adaptive Filtering Configurations 70

6.2 The Complex Least Mean Square Algorithm 73

6.3 Nonlinear Feedforward Complex Adaptive Filters 80

6.4 Normalisation of Learning Algorithms 85

6.5 Performance of Feedforward Nonlinear Adaptive Filters 87

6.6 Summary: Choice of a Nonlinear Adaptive Filter 89

7 Adaptive Filters with Feedback 91

7.1 Training of IIR Adaptive Filters 92

7.2 Nonlinear Adaptive IIR Filters: Recurrent Perceptron 97

7.3 Training of Recurrent Neural Networks 99

7.4 Simulation Examples 102

8 Filters with an Adaptive Stepsize 107

8.1 Benveniste Type Variable Stepsize Algorithms 108

8.2 Complex Valued GNGD Algorithms 110

8.3 Simulation Examples 113

9 Filters with an Adaptive Amplitude of Nonlinearity 119

9.1 Dynamical Range Reduction 119

9.2 FIR Adaptive Filters with an Adaptive Nonlinearity 121

9.3 Recurrent Neural Networks with Trainable Amplitude of Activation Functions 122

9.4 Simulation Results 124

10 Data-reusing Algorithms for Complex Valued Adaptive Filters 129

10.1 The Data-reusing Complex Valued Least Mean Square (DRCLMS) Algorithm 129

10.2 Data-reusing Complex Nonlinear Adaptive Filters 131

10.3 Data-reusing Algorithms for Complex RNNs 134

11 Complex Mappings and M¨obius Transformations 137

11.1 Matrix Representation of a Complex Number 137

11.2 The M¨obius Transformation 140

11.3 Activation Functions and M¨obius Transformations 142

11.4 All-pass Systems as M¨obius Transformations 146

11.5 Fractional Delay Filters 147

12 Augmented Complex Statistics 151

12.1 Complex Random Variables (CRV) 152

12.2 Complex Circular Random Variables 158

12.3 Complex Signals 159

12.4 Second-order Characterisation of Complex Signals 161

13 Widely Linear Estimation and Augmented CLMS (ACLMS) 169

13.1 Minimum Mean Square Error (MMSE) Estimation in C 169

13.2 Complex White Noise 172

13.3 Autoregressive Modelling in C 173

13.4 The Augmented Complex LMS (ACLMS) Algorithm 175

13.5 Adaptive Prediction Based on ACLMS 178

14 Duality Between Complex Valued and Real Valued Filters 183

14.1 A Dual Channel Real Valued Adaptive Filter 184

14.2 Duality Between Real and Complex Valued Filters 186

14.3 Simulations 188

15 Widely Linear Filters with Feedback 191

15.1 The Widely Linear ARMA (WL-ARMA) Model 192

15.2 Widely Linear Adaptive Filters with Feedback 192

15.4 The Augmented Kalman Filter Algorithm for RNNs 198

15.5 Augmented Complex Unscented Kalman Filter (ACUKF) 200

15.6 Simulation Examples 203

16 Collaborative Adaptive Filtering 207

16.1 Parametric Signal Modality Characterisation 207

16.2 Standard Hybrid Filtering in R 209

16.3 Tracking the Linear/Nonlinear Nature of Complex Valued Signals 210

16.4 Split vs Fully Complex Signal Natures 214

16.5 Online Assessment of the Nature of Wind Signal 216

16.6 Collaborative Filters for General Complex Signals 217

17 Adaptive Filtering Based on EMD 221

17.1 The Empirical Mode Decomposition Algorithm 222

17.2 Complex Extensions of Empirical Mode Decomposition 226

17.3 Addressing the Problem of Uniqueness 230

17.4 Applications of Complex Extensions of EMD 230

18 Validation of Complex Representations – Is This Worthwhile? 233

18.1 Signal Modality Characterisation in R 234

18.2 Testing for the Validity of Complex Representation 239

18.3 Quantifying Benefits of Complex Valued Representation 243

Appendix A: Some Distinctive Properties of Calculus in C 245

Appendix B: Liouville's Theorem 251

Appendix C: Hypercomplex and Clifford Algebras 253

Appendix D: Real Valued Activation Functions 257

Appendix E: Elementary Transcendental Functions (ETF) 259

Appendix F: The O Notation and Standard Vector and Matrix Differentiation 263

Appendix G: Notions From Learning Theory 265

Appendix H: Notions from Approximation Theory 269

Appendix I: Terminology Used in the Field of Neural Networks 273

Appendix J: Complex Valued Pipelined Recurrent Neural Network (CPRNN) 275

Appendix K: Gradient Adaptive Step Size (GASS) Algorithms in R 279

Appendix L: Derivation of Partial Derivatives from Chapter 8 283

Appendix M: A Posteriori Learning 287

Appendix N: Notions from Stability Theory 291

Appendix O: Linear Relaxation 293

Appendix P: Contraction Mappings, Fixed Point Iteration and Fractals 299

References 309

Index 321

Product Details

ISBN:
9780470066355
Author:
Mandic, Danilo P.
Publisher:
John Wiley & Sons
Author:
Goh, Vanessa
Author:
Mandic, Danilo
Author:
Goh, Vanessa (Su Lee)
Subject:
Waves & Wave Mechanics
Subject:
Neural networks (computer science)
Subject:
Nonlinear theories
Subject:
Telecommunications
Subject:
Functions of complex variables
Subject:
Signal processing
Subject:
Physics-General
Copyright:
Edition Description:
WOL online Book (not BRO)
Series:
Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control
Series Volume:
59
Publication Date:
20090420
Binding:
HARDCOVER
Grade Level:
General/trade
Language:
English
Illustrations:
Y
Pages:
344
Dimensions:
244 x 168 x 15 mm 26 oz

Related Subjects

Engineering » Communications » Telephony
Health and Self-Help » Health and Medicine » Medical Specialties
Science and Mathematics » Chemistry » Organic
Science and Mathematics » Electricity » General Electronics
Science and Mathematics » Physics » General

Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models (Adaptive and Learning Systems for Signal Processing, Communications and Control) New Hardcover
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$161.50 Backorder
Product details 344 pages John Wiley & Sons - English 9780470066355 Reviews:
"Synopsis" by , 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.
"Synopsis" by , This book examines nonlinear adaptive filtering in the complex domain, providing theoretical information and computational principles for optimizing applications in a range of fields. It begins with a full introduction to the topic, including background theory on standard complex statistics. The authors then go on to discuss the theoretical principles of complex valued nonlinear adaptive filters, and the concept of nonlinearity in general, before presenting learning algorithms for recurrent neural networks (RNN). The authors then use this fundamental information to cover more advanced topics such as nonlinear adaptive prediction and forecasting through simulation, and a statistical framework for detecting the nature of complex random variables. The final chapter sets out potential applications using these techniques in order to illustrate the benefit of this approach.
  • Provides a rigorous theoretical foundation for efficient adaptive processing of complex signals, illustrating the benefit of this approach for a number of practical applications
  • Presents theoretical and computational principles for signal processing in the complex domain, which is essential for the optimization of a range of important applications in the fields of radar, sonar, communications, acoustics and GIS. 
  • Includes practical and comprehensive simulation examples, conducted on both benchmark and real-world data, such as wind and radar measurements. 
  • Sets out a statistical framework for detecting the nature of complex random variables. 
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