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Optimal Event-Triggered Control Using Adaptive Dynamic Programming
Optimal Event-triggered Control using Adaptive Dynamic Programming discusses event triggered controller design which includes optimal control and event sampling design for linear and nonlinear dynamic systems including networked control systems (NCS) when the system dynamics are both known and uncertain. The NCS are a first step to realize cyber-physical systems (CPS) or industry 4.0 vision. The authors apply several powerful modern control techniques to the design of event-triggered controllers and derive event-trigger condition and demonstrate closed-loop stability. Detailed derivations, rigorous stability proofs, computer simulation examples, and downloadable MATLAB® codes are included f...
Advances in Neural Networks – ISNN 2012
The two-volume set LNCS 7367 and 7368 constitutes the refereed proceedings of the 9th International Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised full papers presented were carefully reviewed and selected from numerous submissions. The contributions are structured in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; learning algorithms; optimization; pattern recognition; vision; image processing; information processing; neurocontrol; and novel applications.
International Conference on Intelligent Computing: Intelligent computing
This book constitutes the refereed proceedings of the International Conference on Intelligent Computing, ICIC 2006, held in Kunming, China, August 2006. The book collects 161 carefully chosen and revised full papers. Topical sections include neural networks, evolutionary computing and genetic algorithms, kernel methods, combinatorial and numerical optimization, multiobjective evolutionary algorithms, neural optimization and dynamic programming, as well as case-based reasoning and probabilistic reasoning.
Neural Information Processing
The five volume set LNCS 7663, LNCS 7664, LNCS 7665, LNCS 7666 and LNCS 7667 constitutes the proceedings of the 19th International Conference on Neural Information Processing, ICONIP 2012, held in Doha, Qatar, in November 2012. The 423 regular session papers presented were carefully reviewed and selected from numerous submissions. These papers cover all major topics of theoretical research, empirical study and applications of neural information processing research. The 5 volumes represent 5 topical sections containing articles on theoretical analysis, neural modeling, algorithms, applications, as well as simulation and synthesis.
Proceedings of International Conference on Information Technology and Applications
This book includes high-quality papers presented at 16th International Conference on Information Technology and Applications (ICITA 2022), held in Lisbon, Portugal during October 20–22, 2022. The book presents original research work of academics and industry professionals to exchange their knowledge of the state-of-the-art research and development in information technology and applications. The topics covered in the book are cloud computing, business process engineering, machine learning, evolutionary computing, big data analytics, Internet of things and cyber-physical systems, information and knowledge management, computer vision and image processing, computer graphics and games programming, mobile computing, ontology engineering, software and systems modeling, human–computer interaction, online learning / e-learning, computer networks, and web engineering.
Intelligent Computing in Signal Processing and Pattern Recognition
This 1179-page book assembles the complete contributions to the International Conference on Intelligent Computing, ICIC 2006: one volume of Lecture Notes in Computer Science (LNCS); one of Lecture Notes in Artificial Intelligence (LNAI); one of Lecture Notes in Bioinformatics (LNBI); and two volumes of Lecture Notes in Control and Information Sciences (LNCIS). Include are 149 revised full papers, and a Special Session on Computing for Searching Strategies to Control Dynamic Processes.
Intelligent Control and Automation
Results of the International Conference on Intelligent Computing, ICIC 2006: Lecture Notes in Computer Science (LNCS), Lecture Notes in Artificial Intelligence (LNAI), Lecture Notes in Bioinformatics (LNBI), Lecture Notes in Control and Information Sciences (LNCIS). 142 revised full papers are organized in topical sections: Blind Source Separation; Intelligent Sensor Networks; Intelligent Control and Automation; and Data Fusion, Knowledge Discovery, and Data Mining. Includes a Special Session on Smart and Intelligent Home Technology.
Hua Loo-Keng
The distinguished mathematician, Hua Loo-Keng, led an eventful and interesting life through a particularly difficult period in China. This biography, originally published in Chinese in 1995, looks in detail at the transformation of Hua from a self-taught individual into a world-renown mathematician. Hua held a prominent position in the contemporary mathematics circle in China, and thus, this account of his life can be read as in invaluable historical evaluation of the development of mathematics in China, especially during tumultuous events such as the Cultural Revolution and the Great Leap Forward. Besides giving a vivid illustration of Hua's research work in number theory and of his attitude towards and philosophy behind the learning and teaching of mathematics, the author, a student and close friend of Hua's, delves into the latter's tireless efforts in popularizing mathematics in China until his death in 1985.
Self-Learning Optimal Control of Nonlinear Systems
This book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which quantitatively obtain the optimal control schemes of the systems. It analyzes the properties identified by the programming methods, including the convergence of the iterative value functions and the stability of the system under iterative control laws, helping to guarantee the effectiveness of the methods developed. When the system model is known, self-learning optimal control is designed on the basis of the system model; when the system model is not known, adaptive dynamic programming is implemented according to the system data, effectively making the performance of the system converge to the optimum. With various real-world examples to complement and substantiate the mathematical analysis, the book is a valuable guide for engineers, researchers, and students in control science and engineering.
Adaptive Dynamic Programming with Applications in Optimal Control
This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approa...
