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混沌信號(hào)的重構(gòu)及其在基于混沌的通信中的應(yīng)用

混沌信號(hào)的重構(gòu)及其在基于混沌的通信中的應(yīng)用

定 價(jià):¥58.00

作 者: 馮久超,謝智剛 著
出版社: 清華大學(xué)出版社
叢編項(xiàng):
標(biāo) 簽: 通信理論

ISBN: 9787302121206 出版時(shí)間: 2007-11-01 包裝: 平裝
開(kāi)本: 32開(kāi) 頁(yè)數(shù): 218 字?jǐn)?shù):  

內(nèi)容簡(jiǎn)介

  《混沌信號(hào)的重構(gòu)及其在于混沌的通信中的應(yīng)用》系統(tǒng)地總結(jié)了作者近年來(lái)涉及的嵌入在基于混沌的通信中的最基本問(wèn)題——混沌信號(hào)的重構(gòu)及其應(yīng)用的研究成果,覆蓋了關(guān)于混純信號(hào)重構(gòu)的基本問(wèn)題和幾個(gè)高層次的問(wèn)題。共分為四個(gè)層次進(jìn)行深入的論述:1在無(wú)噪聲的信道里如何重構(gòu)原始混沌信號(hào),這是將Takens定理推廣到時(shí)變的連續(xù)時(shí)間與離散時(shí)間系統(tǒng),2如何通過(guò)噪聲污染的混沌信號(hào)重構(gòu)原始混沌系統(tǒng)的動(dòng)力學(xué),3如何通過(guò)噪聲污染同時(shí)也被信道畸變的混沌信號(hào)重構(gòu)原始混沌系統(tǒng)的動(dòng)力學(xué),4基于混沌同步的思想重構(gòu)原始混沌系統(tǒng)的動(dòng)力學(xué)。所有這些問(wèn)題都是圍繞基于混沌的寬帶通信在實(shí)際通信環(huán)境里的實(shí)現(xiàn)為研究目的。該書對(duì)于信號(hào)與信息處理、非線性電路、通信、智能信息處理和自動(dòng)化等學(xué)科的大專院校高年級(jí)學(xué)生和有關(guān)研究人員具有很強(qiáng)的可讀性和重要的參考價(jià)值,并能起到拋起引玉的作用。

作者簡(jiǎn)介

暫缺《混沌信號(hào)的重構(gòu)及其在基于混沌的通信中的應(yīng)用》作者簡(jiǎn)介

圖書目錄

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 NeuralNetworks
3.1 Motivation
3.2 Benefits of NeuralNetworks
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
5.3 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 of the 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 Control
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

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