Combination Of Uncertain Ordinal Expert Statements The Combination Rule Eidmr And Its Application To Low Voltage Grid Classification With Svm PDF Download
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Author | : Sebastian Breker |
Publisher | : Infinite Study |
Total Pages | : 10 |
Release | : |
Genre | : |
ISBN | : |
Download Combination of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM Book in PDF, ePub and Kindle
The use of expert knowledge is always more or less afflicted with uncertainties for many reasons: Expert knowledge may be imprecise, imperfect, or erroneous, for instance. If we ask several experts to label data (e.g., to assign class labels to given data objects, i.e. samples), we often state that these experts make different, sometimes conflicting statements.
Author | : Ronald R. Yager |
Publisher | : Springer |
Total Pages | : 813 |
Release | : 2008-01-22 |
Genre | : Technology & Engineering |
ISBN | : 354044792X |
Download Classic Works of the Dempster-Shafer Theory of Belief Functions Book in PDF, ePub and Kindle
This is a collection of classic research papers on the Dempster-Shafer theory of belief functions. The book is the authoritative reference in the field of evidential reasoning and an important archival reference in a wide range of areas including uncertainty reasoning in artificial intelligence and decision making in economics, engineering, and management. The book includes a foreword reflecting the development of the theory in the last forty years.
Author | : Witold Pedrycz |
Publisher | : Springer Nature |
Total Pages | : 360 |
Release | : 2019-10-23 |
Genre | : Technology & Engineering |
ISBN | : 3030317609 |
Download Deep Learning: Algorithms and Applications Book in PDF, ePub and Kindle
This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
Author | : Huaguang Zhang |
Publisher | : Springer Science & Business Media |
Total Pages | : 432 |
Release | : 2012-12-14 |
Genre | : Technology & Engineering |
ISBN | : 144714757X |
Download Adaptive Dynamic Programming for Control Book in PDF, ePub and Kindle
There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming in Discrete Time approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural networks and techniques of value and policy iteration. The text features three main aspects of ADP in which the methods proposed for stabilization and for tracking and games benefit from the incorporation of optimal control methods: • infinite-horizon control for which the difficulty of solving partial differential Hamilton–Jacobi–Bellman equations directly is overcome, and proof provided that the iterative value function updating sequence converges to the infimum of all the value functions obtained by admissible control law sequences; • finite-horizon control, implemented in discrete-time nonlinear systems showing the reader how to obtain suboptimal control solutions within a fixed number of control steps and with results more easily applied in real systems than those usually gained from infinite-horizon control; • nonlinear games for which a pair of mixed optimal policies are derived for solving games both when the saddle point does not exist, and, when it does, avoiding the existence conditions of the saddle point. Non-zero-sum games are studied in the context of a single network scheme in which policies are obtained guaranteeing system stability and minimizing the individual performance function yielding a Nash equilibrium. In order to make the coverage suitable for the student as well as for the expert reader, Adaptive Dynamic Programming in Discrete Time: • establishes the fundamental theory involved clearly with each chapter devoted to a clearly identifiable control paradigm; • demonstrates convergence proofs of the ADP algorithms to deepen understanding of the derivation of stability and convergence with the iterative computational methods used; and • shows how ADP methods can be put to use both in simulation and in real applications. This text will be of considerable interest to researchers interested in optimal control and its applications in operations research, applied mathematics computational intelligence and engineering. Graduate students working in control and operations research will also find the ideas presented here to be a source of powerful methods for furthering their study.
Author | : Frank L. Lewis |
Publisher | : John Wiley & Sons |
Total Pages | : 498 |
Release | : 2013-01-28 |
Genre | : Technology & Engineering |
ISBN | : 1118453972 |
Download Reinforcement Learning and Approximate Dynamic Programming for Feedback Control Book in PDF, ePub and Kindle
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.
Author | : Natividad Martínez Madrid |
Publisher | : Springer Science & Business Media |
Total Pages | : 294 |
Release | : 2009-02-18 |
Genre | : Technology & Engineering |
ISBN | : 1402098235 |
Download Intelligent Technical Systems Book in PDF, ePub and Kindle
Intelligent technical systems are networked, embedded systems incorporating real-time capacities that are able to interact with and adapt to their environments. These systems need innovative approaches in order to meet requirements like cost, size, power and memory consumption, as well as real-time compliance and security. Intelligent Technical Systems covers different levels like multimedia systems, embedded programming, middleware platforms, sensor networks and autonomous systems and applications for intelligent engineering. Each level is discussed by a set of original articles summarizing the state of the art and presenting a concrete application; they include a deep discussion of their model and explain all design decisions relevant to obtain a mature solution.
Author | : Alberto Del Bimbo |
Publisher | : Springer Nature |
Total Pages | : 741 |
Release | : 2021-02-20 |
Genre | : Computers |
ISBN | : 3030687635 |
Download Pattern Recognition. ICPR International Workshops and Challenges Book in PDF, ePub and Kindle
This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.
Author | : Florentin Smarandache |
Publisher | : |
Total Pages | : 110 |
Release | : 1998 |
Genre | : Mathematics |
ISBN | : |
Download Neutrosophy Book in PDF, ePub and Kindle
Author | : Jing Liu |
Publisher | : Springer |
Total Pages | : 0 |
Release | : 2012-05-23 |
Genre | : Computers |
ISBN | : 9783642306860 |
Download Advances in Computational Intelligence Book in PDF, ePub and Kindle
This state-of-the-art survey offers a renewed and refreshing focus on the progress in evolutionary computation, in neural networks, and in fuzzy systems. The book presents the expertise and experiences of leading researchers spanning a diverse spectrum of computational intelligence in these areas. The result is a balanced contribution to the research area of computational intelligence that should serve the community not only as a survey and a reference, but also as an inspiration for the future advancement of the state of the art of the field. The 13 selected chapters originate from lectures and presentations given at the IEEE World Congress on Computational Intelligence, WCCI 2012, held in Brisbane, Australia, in June 2012.
Author | : Zhi-Hua Zhou |
Publisher | : Springer |
Total Pages | : 361 |
Release | : 2019-05-22 |
Genre | : Computers |
ISBN | : 9811359563 |
Download Evolutionary Learning: Advances in Theories and Algorithms Book in PDF, ePub and Kindle
Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.