Neural Approximations For Optimal Control And Decision
Neural Approximations For Optimal Control And Decision Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Neural Approximations For Optimal Control And Decision book. This book definitely worth reading, it is an incredibly well-written.
Neural Approximations for Optimal Control and Decision by Riccardo Zoppoli,Marcello Sanguineti,Giorgio Gnecco,Thomas Parisini Pdf
Neural Approximations for Optimal Control and Decision provides a comprehensive methodology for the approximate solution of functional optimization problems using neural networks and other nonlinear approximators where the use of traditional optimal control tools is prohibited by complicating factors like non-Gaussian noise, strong nonlinearities, large dimension of state and control vectors, etc. Features of the text include: • a general functional optimization framework; • thorough illustration of recent theoretical insights into the approximate solutions of complex functional optimization problems; • comparison of classical and neural-network based methods of approximate solution; • bounds to the errors of approximate solutions; • solution algorithms for optimal control and decision in deterministic or stochastic environments with perfect or imperfect state measurements over a finite or infinite time horizon and with one decision maker or several; • applications of current interest: routing in communications networks, traffic control, water resource management, etc.; and • numerous, numerically detailed examples. The authors’ diverse backgrounds in systems and control theory, approximation theory, machine learning, and operations research lend the book a range of expertise and subject matter appealing to academics and graduate students in any of those disciplines together with computer science and other areas of engineering.
Topics on System Analysis and Integrated Water Resources Management by Andrea Castelletti,Rodolfo Soncini-Sessa Pdf
The Integrated Water Resources Management (IWRM) paradigm has been worldwide recognized as the only feasible way currently available to ensure a sustainable perspective in planning and managing water resource systems. It is the inspiring principle of the Water Framework Directive, adopted by the European Union in 2000, as well as the main reference for all the water related activity of UNESCO in the third world countries. However, very often, real world attempts of implementing IWRM fail for the lack of a systematic approach and the inadequacy of tools and techniques adopted to address the intrinsically complex nature of water systems. This book explores recent and important contributions of System Analysis and Control Theory to the technical application of such paradigm and to the improvement of its theoretical basis. Its prior aim is to demonstrate how the modelling and computational difficulties posed by this paradigm might be significantly reduced by strengthening the efficiency of the solution techniques, instead of weakening the integration requirements. The first introductory chapter provides the reader with a logical map of the book, by formalizing the IWRM paradigm in a nine-step decisional procedure and by identifying the points where the contribution of System Analysis and Control Theory is more useful. The book is then organized in three sections whose chapters analyze some theoretical and mathematical aspects of these contributions or presents design applications. The outstanding research issues on the border between System Analysis and IWRM is depicted in the last chapter, where a pull of scientists and experts, coordinated by Prof. Tony Jakeman describe the foreseeable scenario. The book is based on the most outstanding contributions to the IFAC workshop on Modelling and Control for Participatory Planning and Managing Water Systems held in Venice, September 28- October 1, 2004. That workshop has been conceived and organized with the explicit purpose of producing this book: the maximum length of the papers was unusually long (of the size of a book chapter) and only five long oral presentations were planned each day, thus allowing for a very useful and constructive discussion. Contributions from the leading world specialists of the field Integration of technical modelling aspects and participatory decision-making Good compromise between theory and application
Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control by Dimitri Bertsekas Pdf
The purpose of this book is to propose and develop a new conceptual framework for approximate Dynamic Programming (DP) and Reinforcement Learning (RL). This framework centers around two algorithms, which are designed largely independently of each other and operate in synergy through the powerful mechanism of Newton's method. We call these the off-line training and the on-line play algorithms; the names are borrowed from some of the major successes of RL involving games. Primary examples are the recent (2017) AlphaZero program (which plays chess), and the similarly structured and earlier (1990s) TD-Gammon program (which plays backgammon). In these game contexts, the off-line training algorithm is the method used to teach the program how to evaluate positions and to generate good moves at any given position, while the on-line play algorithm is the method used to play in real time against human or computer opponents. Both AlphaZero and TD-Gammon were trained off-line extensively using neural networks and an approximate version of the fundamental DP algorithm of policy iteration. Yet the AlphaZero player that was obtained off-line is not used directly during on-line play (it is too inaccurate due to approximation errors that are inherent in off-line neural network training). Instead a separate on-line player is used to select moves, based on multistep lookahead minimization and a terminal position evaluator that was trained using experience with the off-line player. The on-line player performs a form of policy improvement, which is not degraded by neural network approximations. As a result, it greatly improves the performance of the off-line player. Similarly, TD-Gammon performs on-line a policy improvement step using one-step or two-step lookahead minimization, which is not degraded by neural network approximations. To this end it uses an off-line neural network-trained terminal position evaluator, and importantly it also extends its on-line lookahead by rollout (simulation with the one-step lookahead player that is based on the position evaluator). Significantly, the synergy between off-line training and on-line play also underlies Model Predictive Control (MPC), a major control system design methodology that has been extensively developed since the 1980s. This synergy can be understood in terms of abstract models of infinite horizon DP and simple geometrical constructions, and helps to explain the all-important stability issues within the MPC context. An additional benefit of policy improvement by approximation in value space, not observed in the context of games (which have stable rules and environment), is that it works well with changing problem parameters and on-line replanning, similar to indirect adaptive control. Here the Bellman equation is perturbed due to the parameter changes, but approximation in value space still operates as a Newton step. An essential requirement here is that a system model is estimated on-line through some identification method, and is used during the one-step or multistep lookahead minimization process. In this monograph we aim to provide insights (often based on visualization), which explain the beneficial effects of on-line decision making on top of off-line training. In the process, we will bring out the strong connections between the artificial intelligence view of RL, and the control theory views of MPC and adaptive control. Moreover, we will show that in addition to MPC and adaptive control, our conceptual framework can be effectively integrated with other important methodologies such as multiagent systems and decentralized control, discrete and Bayesian optimization, and heuristic algorithms for discrete optimization. One of our principal aims is to show, through the algorithmic ideas of Newton's method and the unifying principles of abstract DP, that the AlphaZero/TD-Gammon methodology of approximation in value space and rollout applies very broadly to deterministic and stochastic optimal control problems. Newton's method here is used for the solution of Bellman's equation, an operator equation that applies universally within DP with both discrete and continuous state and control spaces, as well as finite and infinite horizon.
Engineering Mathematics and Computing by Park Gyei-Kark,Dipak Kumar Jana,Prabir Panja,Mohd Helmy Abd Wahab Pdf
This book contains select papers presented at the 3rd International Conference on Engineering Mathematics and Computing (ICEMC 2020), held at the Haldia Institute of Technology, Purba Midnapur, West Bengal, India, from 5–7 February 2020. The book discusses new developments and advances in the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, hybrid intelligent systems, etc. The book, containing 19 chapters, is useful to the researchers, scholars, and practising engineers as well as graduate students of engineering and applied sciences.
Advances in Computing, Informatics, Networking and Cybersecurity by Petros Nicopolitidis,Sudip Misra,Laurence T. Yang,Bernard Zeigler,Zhaolng Ning Pdf
This book presents new research contributions in the above-mentioned fields. Information and communication technologies (ICT) have an integral role in today’s society. Four major driving pillars in the field are computing, which nowadays enables data processing in unprecedented speeds, informatics, which derives information stemming for processed data to feed relevant applications, networking, which interconnects the various computing infrastructures and cybersecurity for addressing the growing concern for secure and lawful use of the ICT infrastructure and services. Its intended readership covers senior undergraduate and graduate students in Computer Science and Engineering and Electrical Engineering, as well as researchers, scientists, engineers, ICT managers, working in the relevant fields and industries.
Handbook on Neural Information Processing by Monica Bianchini,Marco Maggini,Lakhmi C. Jain Pdf
This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.
Neural Systems for Control by Omid Omidvar,David L. Elliott Pdf
Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance. Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory Represents the most up-to-date developments in this rapidly growing application area of neural networks Takes a new and novel approach to system identification and synthesis
SOFSEM 2010: Theory and Practice of Computer Science by Jan van Leeuwen,Anca Muscholl,David Peleg,Jaroslav Pokorny,Bernhard Rumpe Pdf
This book constitutes the refereed proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2010, held in Špindleruv Mlýn, Czech Republic, in January 2009. The 53 revised full papers, presented together with 11 invited contributions, were carefully reviewed and selected from 134 submissions. SOFSEM 2010 was organized around the following four tracks: Foundations of computer science, principles of software construction, Data, knowledge, and intelligent systems and Web science.
Integrated and Participatory Water Resources Management - Theory by Rodolfo Soncini-Sessa,Enrico Weber,Andrea Castelletti Pdf
Covering the more recent advances in Modelling, Planning, Management and Negotiations for Integrated Water Resource Management, this text brings together knowledge and concepts from Hydrology, System Analysis, Control Theory, Conflict Resolution, and Decision and Negotiation Theory. Without compromising on mathematical rigour, the book maintains a fine line between theory and application, methodology and tools, avoiding getting locked into excessively theoretical and formal development of the issues discussed. The non-technical aspects of water resource systems (such as societal, political and legal concerns) are recognized throughout the book as having a great, if not fundamental, importance to reaching an agreed-upon decision; they are therefore integrated into the more technical and mathematical issues. The book provides a unified, coordinated and comprehensive framework that will facilitate the increasingly appropriate application of the Integrated Water Resource Management paradigm by current and future practising professionals, decision-makers and scientists. · Integration of technical modelling and control aspects with participatory and decision-making issues· Insightful and comprehensive treatment of theoretical contents, supported by practical examples· A wide collection of exercises and project examples based on real-world case studies (with complete solutions)
Optimization and Decision Science: Operations Research, Inclusion and Equity by Paola Cappanera,Matteo Lapucci,Fabio Schoen,Marco Sciandrone,Fabio Tardella,Filippo Visintin Pdf
This volume collects peer-reviewed short papers presented at the Optimization and Decision Science conference (ODS 2022) held in Florence (Italy) from August 30th to September 2nd, 2022, organized by the Global Optimization Laboratory within the University of Florence and AIRO (the Italian Association for Operations Research). The book includes contributions in the fields of operations research, optimization, problem solving, decision making and their applications in the most diverse domains. Moreover, a special focus is set on the challenging theme Operations Research: inclusion and equity. The work offers 30 contributions, covering a wide spectrum of methodologies and applications. Specifically, they feature the following topics: (i) Variational Inequalities, Equilibria and Games, (ii) Optimization and Machine Learning, (iii) Global Optimization, (iv) Optimization under Uncertainty, (v) Combinatorial Optimization, (vi) Transportation and Mobility, (vii) Health Care Management, and (viii) Applications. This book is primarily addressed to researchers and PhD students of the operations research community. However, due to its interdisciplinary content, it will be of high interest for other closely related research communities.
Advances in Optimization and Decision Science for Society, Services and Enterprises by Massimo Paolucci,Anna Sciomachen,Pierpaolo Uberti Pdf
The contributions included in the volume are drawn from presentations at ODS2019 – International Conference on Optimization and Decision Science, which was the 49th annual meeting of the Italian Operations Research Society (AIRO) held at Genoa, Italy, on 4-7 September 2019. This book presents very recent results in the field of Optimization and Decision Science. While the book is addressed primarily to the Operations Research (OR) community, the interdisciplinary contents ensure that it will also be of very high interest for scholars and researchers from many scientific disciplines, including computer sciences, economics, mathematics, and engineering. Operations Research is known as the discipline of optimization applied to real-world problems and to complex decision-making fields. The focus is on mathematical and quantitative methods aimed at determining optimal or near-optimal solutions in acceptable computation times. This volume not only presents theoretical results but also covers real industrial applications, making it interesting for practitioners facing decision problems in logistics, manufacturing production, and services. Readers will accordingly find innovative ideas from both a methodological and an applied perspective.
Adaptive Dynamic Programming with Applications in Optimal Control by Derong Liu,Qinglai Wei,Ding Wang,Xiong Yang,Hongliang Li Pdf
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.
