Contents -- Preface -- Acknowledgements -- 1 Chaos and Communications -- 1.1 Historical Account -- 1.2 Chaos -- 1.3 Quantifying Chaotic Behavior -- 1.3.1 Lyapunov Exponents for Continuous-Time Nonlinear Systems -- 1.3.2 Lyapunov Exponent for Discrete-Time Systems -- 1.3.3 Kolmogorov Entropy -- 1.3.4 Attractor Dimension -- 1.4 Properties of Chaos -- 1.5 Chaos-Based Communications -- 1.5.1 Conventional Spread Spectrum -- 1.5.2 Spread Spectrum with Chaos -- 1.5.3 Chaotic Synchronization -- 1.6 Communications Using Chaos as Carriers -- 1.6.1 Chaotic Masking Modulation -- 1.6.2 Dynamical Feedback Modulation -- 1.6.3 Inverse System Modulation -- 1.6.4 Chaotic Modulation -- 1.6.5 Chaos Shift Keying -- 1.6.6 Differential Chaos Shift Keying Modulation -- 1.7 Remarks on Chaos-Based Communications -- 1.7.1 Security Issues -- 1.7.2 Engineering Challenges -- 2 Reconstruction of Signals -- 2.1 Reconstruction of System Dynamics -- 2.1.1 Topological Embeddings -- 2.1.2 Delay Coordinates -- 2.2 Differentiable Embeddings -- 2.3 Phase Space Reconstruction-Example -- 2.4 Problems and Research Approaches -- 3 Fundamentals of Neural Networks -- 3.1 Motivation -- 3.2 Benefits of Neural Networks -- 3.3 Radial Basis Function Neural Networks -- 3.3.1 Background Theory -- 3.3.2 Research Progress in Radial Basis Function Networks -- 3.4 Recurrent Neural Networks -- 3.4.1 Introduction -- 3.4.2 Topology of the Recurrent Networks -- 3.4.3 Learning Algorithms -- 4 Signal Reconstruction in Noisefree and Distortionless Channels -- 4.1 Reconstruction of Attractor for Continuous Time-Varying Systems -- 4.2 Reconstruction and Observability -- 4.3 Communications Based on Reconstruction Approach -- 4.3.1 Parameter Estimations -- 4.3.2 Information Retrievals -- 4.4 Reconstruction of Attractor for Discrete Time-Varying Systems -- 4.5 Summary.

5 Signal Reconstruction from a Filtering Viewpoint: Theory -- 5.1 The Kalman Filter and Extended Kalman Filter -- 5.1.1 The Kalman Filter -- 5.1.2 Extended Kalman Filter -- 5.2 The Unscented Kalman Filter -- 5.2.1 The Unscented Kalman Filtering Algorithm -- 5.2.2 Convergence Analysis for the UKF Algorithm -- 5.2.3 Computer Simulations -- 5.2.3.1 Type 1 -- 5.2.3.2 Type 2 -- 5.2.3.3 Type 3 -- 5.3 Summary -- 6 Signal Reconstruction from a Filtering Viewpoint: Application -- 6.1 Introduction -- 6.2 Filtering of Noisy Chaotic Signals -- 6.2.1 Filtering Algorithm -- 6.2.2 Computer Simulation -- 6.3 Blind Equalization for Fading Channels -- 6.3.1 Modeling of Wireless Communication Channels -- 6.3.2 Blind Equalization of Fading Channels with Fixed Channel Coefficients -- 6.3.3 Blind Equalization for Time-Varying Fading Channels -- 6.4 Summary -- 7 Signal Reconstruction in Noisy Channels -- 7.1 Review of Chaotic Modulation -- 7.2 Formulation of Chaotic Modulation and Demodulation -- 7.3 On-Line Adaptive Learning Algorithm and Demodulation -- 7.3.1 Description ofthe Network -- 7.3.2 Network Growth -- 7.3.3 Network Update with Extended Kalman Filter -- 7.3.4 Pruning of Hidden Units -- 7.3.5 Summary of the Flow of Algorithm -- 7.4 Computer Simulation and Evaluation -- 7.5 Application to Non-coherent Detection in Chaos-Based Communication -- 7.6 Summary -- 8 Signal Reconstruction in Noisy Distorted Channels -- 8.1 Preliminaries -- 8.1.1 Conventional Equalizers -- 8.1.2 Reconstruction of Chaotic Signals and Equalization -- 8.1.3 Recurrent Neural Network and Equalization -- 8.2 Training Algorithm -- 8.3 Simulation Study -- 8.3.1 Chaotic Signal Transmission -- 8.3.2 Filtering Effect of Communication Channels -- 8.3.3 Results -- 8.4 Comparisons and Discussions -- 8.5 Summary -- 9 Chaotic Network Synchronization and Its Applications in Communications.

9.1 Chaotic Network Synchronization -- 9.1.1 Network Synchronization -- 9.1.2 Chaos Contro1. -- 9.1.3 Implementation of the Synchronization Scheme -- 9.2 Implementation of Spread-Spectrum Communications -- 9.2.1 Encoding and Decoding -- 9.2.2 Test Results for Communications -- 9.3 Summary -- 10 Conclusions -- 10.1 Summary of Methods -- 10.2 Further Research -- Bibliography -- Index.