Adaptive Dynamic Programming With Applications In Optimal Control
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Adaptive Dynamic Programming With Applications In Optimal Control
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Author : Derong Liu
language : en
Publisher: Springer
Release Date : 2017-01-04
Adaptive Dynamic Programming With Applications In Optimal Control written by Derong Liu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-04 with Technology & Engineering categories.
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 approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systems is studied using the ADP approach which is then extended to other branches of control theory including decentralized control, robust and guaranteed cost control, and game theory. In the last part of the book the real-world significance of ADP theory is presented, focusing on three application examples developed from the authors’ work: • renewable energy scheduling for smart power grids;• coal gasification processes; and• water–gas shift reactions. Researchers studying intelligent control methods and practitioners looking to apply them in the chemical-process and power-supply industries will find much to interest them in this thorough treatment of an advanced approach to control.
Adaptive Dynamic Programming For Control
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Author : Huaguang Zhang
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-14
Adaptive Dynamic Programming For Control written by Huaguang Zhang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-14 with Technology & Engineering categories.
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 aclearly 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.
Advanced Optimal Control And Applications Involving Critic Intelligence
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Author : Ding Wang
language : en
Publisher: Springer Nature
Release Date : 2023-01-21
Advanced Optimal Control And Applications Involving Critic Intelligence written by Ding Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-21 with Technology & Engineering categories.
This book intends to report new optimal control results with critic intelligence for complex discrete-time systems, which covers the novel control theory, advanced control methods, and typical applications for wastewater treatment systems. Therein, combining with artificial intelligence techniques, such as neural networks and reinforcement learning, the novel intelligent critic control theory as well as a series of advanced optimal regulation and trajectory tracking strategies are established for discrete-time nonlinear systems, followed by application verifications to complex wastewater treatment processes. Consequently, developing such kind of critic intelligence approaches is of great significance for nonlinear optimization and wastewater recycling. The book is likely to be of interest to researchers and practitioners as well as graduate students in automation, computer science, and process industry who wish to learn core principles, methods, algorithms, and applications in the field of intelligent optimal control. It is beneficial to promote the development of intelligent optimal control approaches and the construction of high-level intelligent systems.
Neural Information Processing
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Author : Biao Luo
language : en
Publisher: Springer Nature
Release Date : 2023-11-25
Neural Information Processing written by Biao Luo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-25 with Computers categories.
The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.
Handbook On Computer Learning And Intelligence In 2 Volumes
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Author : Plamen Parvanov Angelov
language : en
Publisher: World Scientific
Release Date : 2022-06-29
Handbook On Computer Learning And Intelligence In 2 Volumes written by Plamen Parvanov Angelov and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-29 with Computers categories.
The Handbook on Computer Learning and Intelligence is a second edition which aims to be a one-stop-shop for the various aspects of the broad research area of computer learning and intelligence. This field of research evolved so much in the last five years that it necessitates this new edition of the earlier Handbook on Computational Intelligence.This two-volume handbook is divided into five parts. Volume 1 covers Explainable AI and Supervised Learning. Volume 2 covers three parts: Deep Learning, Intelligent Control, and Evolutionary Computation. The chapters detail the theory, methodology and applications of computer learning and intelligence, and are authored by some of the leading experts in the respective areas. The fifteen core chapters of the previous edition have been written and significantly refreshed by the same authors. Parts of the handbook have evolved to keep pace with the latest developments in computational intelligence in the areas that span across Machine Learning and Artificial Intelligence. The Handbook remains dedicated to applications and engineering-orientated aspects of these areas over abstract theories.Related Link(s)
Computational Intelligence Volume I
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Author : Hisao Ishibuchi
language : en
Publisher: EOLSS Publications
Release Date : 2015-12-30
Computational Intelligence Volume I written by Hisao Ishibuchi and has been published by EOLSS Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-30 with categories.
Computational intelligence is a component of Encyclopedia of Technology, Information, and Systems Management Resources in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. Computational intelligence is a rapidly growing research field including a wide variety of problem-solving techniques inspired by nature. Traditionally computational intelligence consists of three major research areas: Neural Networks, Fuzzy Systems, and Evolutionary Computation. Neural networks are mathematical models inspired by brains. Neural networks have massively parallel network structures with many neurons and weighted connections. Whereas each neuron has a simple input-output relation, a neural network with many neurons can realize a highly non-linear complicated mapping. Connection weights between neurons can be adjusted in an automated manner by a learning algorithm to realize a non-linear mapping required in a particular application task. Fuzzy systems are mathematical models proposed to handle inherent fuzziness in natural language. For example, it is very difficult to mathematically define the meaning of “cold” in everyday conversations such as “It is cold today” and “Can I have cold water”. The meaning of “cold” may be different in a different situation. Even in the same situation, a different person may have a different meaning. Fuzzy systems offer a mathematical mechanism to handle inherent fuzziness in natural language. As a result, fuzzy systems have been successfully applied to real-world problems by extracting linguistic knowledge from human experts in the form of fuzzy IF-THEN rules. Evolutionary computation includes various population-based search algorithms inspired by evolution in nature. Those algorithms usually have the following three mechanisms: fitness evaluation to measure the quality of each solution, selection to choose good solutions from the current population, and variation operators to generate offspring from parents. Evolutionary computation has high applicability to a wide range of optimization problems with different characteristics since it does not need any explicit mathematical formulations of objective functions. For example, simulation-based fitness evaluation is often used in evolutionary design. Subjective fitness evaluation by a human user is also often used in evolutionary art and music. These volumes are aimed at the following five major target audiences: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers.
Advances In Neural Computation Machine Learning And Cognitive Research Vii
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Author : Boris Kryzhanovsky
language : en
Publisher: Springer Nature
Release Date : 2023-10-11
Advances In Neural Computation Machine Learning And Cognitive Research Vii written by Boris Kryzhanovsky and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-11 with Technology & Engineering categories.
This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXV International Conference on Neuroinformatics, held on October 23-27, 2023, in Moscow, Russia.
Neural Information Processing
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Author : Tom Gedeon
language : en
Publisher: Springer Nature
Release Date : 2019-12-10
Neural Information Processing written by Tom Gedeon and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-10 with Computers categories.
The three-volume set of LNCS 11953, 11954, and 11955 constitutes the proceedings of the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. The 173 full papers presented were carefully reviewed and selected from 645 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The second volume, LNCS 11954, is organized in topical sections on image processing by neural techniques; learning from incomplete data; model compression and optimisation; neural learning models; neural network applications; and social network computing.
Neural Information Processing
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Author : Long Cheng
language : en
Publisher: Springer
Release Date : 2018-12-03
Neural Information Processing written by Long Cheng and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-03 with Computers categories.
The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The 7th and final volume, LNCS 11307, is organized in topical sections on robotics and control; biomedical applications; and hardware.
A Course In Reinforcement Learning 2nd Edition
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Author : Dimitri Bertsekas
language : en
Publisher: Athena Scientific
Release Date : 2024-12-20
A Course In Reinforcement Learning 2nd Edition written by Dimitri Bertsekas and has been published by Athena Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-20 with Computers categories.
This is the 2nd edition of the textbook used at the author's ASU research-oriented course on Reinforcement Learning (RL), offered in each of the last six years. Its purpose is to give an overview of the RL methodology, particularly as it relates to problems of optimal and suboptimal decision and control, as well as discrete optimization. While in this book mathematical proofs are deemphasized, there is considerable related analysis, which supports the conclusions and can be found in the author's recent RL and DP books. These books also contain additional material on off-line training of neural networks, on the use of policy gradient methods for approximation in policy space, and on aggregation.