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The Murder of A Beauty Queen
A beautiful, sensuous and rich widow is brutally murdered in the most questionable of circumstances. The last person to see her alive is her brother-in-law and lover—a man later found guilty on circumstantial evidence. Not until the condemned man appealed did a witness come forward and admit that he had given false evidence. How did she die? Who was the other mysterious lover to whom she constantly penned saucy letters? Why did the witness lie?
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.
Handbook of Learning and Approximate Dynamic Programming
A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation code Provides a tutorial that readers can use to start implementing the learning algorithms provided in the book Includes ideas, directions, and recent results on current research issues and addresses applications where ADP has been successfully implemented The contributors are leading researchers in the field
Multi-Objective Optimization System Designs and Their Applications
This book introduces multi-objective design methods to solve multi-objective optimization problems (MOPs) of linear/nonlinear dynamic systems under intrinsic random fluctuation and external disturbance. The MOPs of multiple targets for systems are all transformed into equivalent linear matrix inequality (LMI)-constrained MOPs. Corresponding reverse-order LMI-constrained multi-objective evolution algorithms are introduced to solve LMI-constrained MOPs using MATLAB®. All proposed design methods are based on rigorous theoretical results, and their applications are focused on more practical engineering design examples. Features: Discusses multi-objective optimization from an engineer’s perspe...
Advances in Neural Networks Research
IJCNN is the flagship conference of the INNS, as well as the IEEE Neural Networks Society. It has arguably been the preeminent conference in the field, even as neural network conferences have proliferated and specialized. As the number of conferences has grown, its strongest competition has migrated away from an emphasis on neural networks. IJCNN has embraced the proliferation of spin-off and related fields (see the topic list, below), while maintaining a core emphasis befitting its name. It has also succeeded in enforcing an emphasis on quality.
Kalman Filter
The aim of this book is to provide an overview of recent developments in Kalman filter theory and their applications in engineering and scientific fields. The book is divided into 24 chapters and organized in five blocks corresponding to recent advances in Kalman filtering theory, applications in medical and biological sciences, tracking and positioning systems, electrical engineering and, finally, industrial processes and communication networks.
MOBIMEDIA 2020
We are delighted to introduce the proceedings of the 13th edition of the 2020 European Alliance for Innovation (EAI) International Conference on Mobile Multimedia Communications (MOBIMEDIA). This conference has brought researchers, developers and practitioners around the world who are leveraging and developing multimedia coding, mobile communications and networking fields. Developing and leveraging multimedia coding, mobile communications and networking fields requires adopting an interdisciplinary approach where multimedia, networking and physical layer issues are addressed jointly. Basic theories, key technologies and Artificial Intelligence for next-generations wireless communications，i...
Interval Analysis
Interval Analysis An innovative and unique application of interval analysis to optimal control problems In Interval Analysis: Application in the Optimal Control Problems, celebrated researcher and engineer Dr. Navid Razmjooy delivers an expert discussion of the uncertainties in the analysis of optimal control problems. In the book, Dr. Razmjooy uses an open-ended approach to solving optimal control problems with indefinite intervals. Utilizing an extended, Runge-Kutta method, the author demonstrates how to accelerate its speed with the piecewise function. You’ll find recursive methods used to achieve more compact answers, as well as how to solve optimal control problems using the interval ...
Model-Based Reinforcement Learning
Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory—optimal control and dynamic programming – or on algorithms—most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning co...
