In this book, the stability analysis and estimator design problems are discussed for delayed discrete-time memristive neural networks. In each chapter, the analysis problems are firstly considered, where the stability, synchronization and other performances (e.g., robustness, disturbances attenuation level) are investigated within a unified theoretical framework. In this stage, some novel notions are put forward to reflect the engineering practice. Then, the estimator design issues are discussed where sufficient conditions are derived to ensure the existence of the desired estimators with guaranteed performances. Finally, the theories and techniques developed in previous parts are applied to deal with some issues in several emerging research areas.
The book
- Unifies existing and emerging concepts concerning delayed discrete memristive neural networks with an emphasis on a variety of network-induced phenomena
- Captures recent advances of theories, techniques, and applications of delayed discrete memristive neural networks from a network-oriented perspective
- Provides a series of latest results in two popular yet interrelated areas, stability analysis and state estimation of neural networks
- Exploits a unified framework for analysis and synthesis by designing new tools and techniques in combination with conventional theories of systems science, control engineering and signal processing
- Gives simulation examples in each chapter to reflect the engineering practice
This book discusses the stability analysis and estimator design problems for discrete-time memristive neural networks subject to time-delays and approaches state estimation from different perspectives. Each chapter includes analysis problems and application of theories and techniques to pertinent research areas.
| Preface |
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xi | |
| Acknowledgment |
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xiii | |
| Authors Biographies |
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xv | |
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xvii | |
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xix | |
| Symbols |
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xxi | |
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1 | (18) |
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1.1 Background on Memristive Neural Networks |
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2 | (7) |
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1.1.1 Memristor and Its Circuit Realization |
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4 | (1) |
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1.1.2 Stability Analysis and State Estimation for MNNs |
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5 | (1) |
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1.1.3 Recent Progress on Several Types of Neural Networks |
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6 | (1) |
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7 | (1) |
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8 | (1) |
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8 | (1) |
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1.2 MNNs subject to Engineering-Oriented Complexities |
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9 | (4) |
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10 | (1) |
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10 | (1) |
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1.2.3 Network-Induced Incomplete Information |
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11 | (1) |
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1.2.3.1 Missing Measurements |
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11 | (1) |
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12 | (1) |
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1.2.3.3 Signal Quantization |
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12 | (1) |
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13 | (3) |
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1.3.1 Event-Triggering Mechanisms |
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13 | (1) |
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1.3.2 Network Communication Protocols |
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14 | (1) |
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14 | (1) |
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15 | (1) |
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15 | (1) |
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1.3.3 Set-Membership Technique |
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15 | (1) |
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1.3.4 Non-Fragile Algorithm |
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16 | (1) |
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16 | (3) |
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2 H∞ State Estimation For Discrete-Time Memristive Recurrent Neural Networks with Stochastic Time-Delays |
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19 | (14) |
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20 | (3) |
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23 | (5) |
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2.3 An Illustrative Example |
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28 | (3) |
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31 | (2) |
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3 Event-Triggered H∞ State Estimation For Delayed Stochastic Memristive Neural Networks with Missing Measurements: the Discrete Time Case |
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33 | (22) |
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34 | (6) |
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40 | (9) |
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3.3 An Illustrative Example |
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49 | (3) |
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52 | (3) |
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4 H∞ State Estimation For Discrete-Time Stochastic Memristive Bam Neural Networks with Mixed Time-Delays |
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55 | (22) |
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4.1 Problem Formulation and Preliminaries |
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56 | (7) |
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63 | (9) |
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72 | (4) |
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76 | (1) |
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5 Stability Analysis For Discrete-Time Stochastic Memristive Neural Networks with Both Leakage and Probabilistic Delays |
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77 | (18) |
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78 | (5) |
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83 | (9) |
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5.3 Illustrative Examples |
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92 | (2) |
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94 | (1) |
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6 Delay-Distribution-Dependent H∞ State Estimation For Discrete-Time Memristive Neural Networks with Mixed Time-Delays and Fading Measurements |
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95 | (22) |
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96 | (6) |
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102 | (10) |
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6.3 Illustrative Examples |
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112 | (3) |
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115 | (2) |
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7 On State Estimation For Discrete Time-Delayed Memristive Neural Networks Under the Wtod Protocol: A Resilient Set-Membership Approach |
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117 | (18) |
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118 | (6) |
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7.1.1 Memristive Neural Network Model |
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118 | (2) |
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120 | (4) |
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124 | (6) |
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7.3 An Illustrative Example |
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130 | (4) |
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134 | (1) |
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8 On Finite-Horizon H∞ State Estimation For Discrete-Time Delayed Memristive Neural Networks Under Stochastic Communication Protocol |
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135 | (16) |
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8.1 Problem Formulation and Preliminaries |
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136 | (4) |
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140 | (6) |
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8.3 An Illustrative Example |
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146 | (3) |
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149 | (2) |
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9 Resilient H∞ State Estimation For Discrete-Time Stochastic Delayed Memristive Neural Networks: A Dynamic Event-Triggered Mechanism |
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151 | (18) |
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152 | (4) |
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156 | (8) |
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9.3 An Illustrative Example |
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164 | (4) |
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168 | (1) |
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10 H∞ and L2 --- L∞ State Estimation For Delayed Memristive Neural Networks On Finite Horizon: the Round-Robin Protocol |
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169 | (22) |
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10.1 Problem Formulation and Preliminaries |
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170 | (3) |
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173 | (9) |
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10.3 An Illustrative Example |
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182 | (8) |
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190 | (1) |
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11 Conclusions and Future Topics |
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191 | (2) |
| Bibliography |
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193 | (20) |
| Index |
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213 | |
Hongjian Liu is currently a Professor in the School of Mathematics and Physics, Anhui Polytechnic University, Wuhu, China. His current research interests include filtering theory, memristive neural networks and network communication systems. He is a very active reviewer for many international journals.
Zidong Wang is currently Professor of Dynamical Systems and Computing at Brunel University London in the United Kingdom. His research interests include dynamical systems, signal processing, bioinformatics, control theory and applications.
Lifeng Ma is currently a Professor with the School of Automation, Nanjing University of Science and Technology, Nanjing, China. His current research interests include nonlinear control and signal processing, variable structure control, distributed control and filtering, time-varying systems, and multi-agent systems.
