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
Present energy systems are undergoing a radical transformation, driven by the urgent need to address the climate change crisis. At the same time, we are witnessing the sharp growth of energy data and a revolution of advanced technologies, with artificial intelligence (AI) and Blockchain emerging as two of the most transformative technologies of our time. The convergence of these two technologies has the potential to create a paradigm shift in the energy sector, enabling the development of smart energy systems that are more resilient, efficient, and sustainable. This book situates itself at the forefront of this paradigm shift, providing a timely and comprehensive guide to AI and Blockchain technologies in the energy system. Moving from an introduction to the basic concepts of smart energy systems, this book proceeds to examine the key challenges facing the energy system, and how AI and Blockchain can be used to address these challenges. Research examples are presented to showcase the role and impact of these new technologies, while the latest developed testbeds are summarised and explained to help researchers accelerate their development of these technologies. This book is an indispensable guide to the current changes in the energy system, being of particular use to industry professionals, from researchers to management, looking to stay ahead of technological developments.
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
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