Dynamic programming

View this topic in
In mathematics and computer science, dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. (Wikipedia.org)






Conferences related to Dynamic programming

Back to Top

2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

Promote the exchange of ideas between academia and industry in the field of computer and networks dependability


2019 IEEE 58th Conference on Decision and Control (CDC)

The CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, systems and control, and related areas.The 58th CDC will feature contributed and invited papers, as well as workshops and may include tutorial sessions.The IEEE CDC is hosted by the IEEE Control Systems Society (CSS) in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).


2019 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2019, the 26th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC2019) will be held in the south of Europe in Bari, one of the most beautiful and historical cities in Italy. The Bari region’s nickname is “Little California” for its nice weather and Bari's cuisine is one of Italian most traditional , based of local seafood and olive oil. SMC2019 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report up-to-the-minute innovations and developments, summarize state­of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems and cybernetics. Advances have importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience, and thereby improve quality of life.


2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


More Conferences

Periodicals related to Dynamic programming

Back to Top

Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Antennas and Wireless Propagation Letters, IEEE

IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.


Applied Superconductivity, IEEE Transactions on

Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission


Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


More Periodicals

Most published Xplore authors for Dynamic programming

Back to Top

Xplore Articles related to Dynamic programming

Back to Top

The Dynamic Programming Approach to the Optimal Control of Goursat-Darboux Systems

[{u'author_order': 1, u'affiliation': u'Department of Mathematics, The University of Alabama, Tuscaloosa, Alabama 35487-0350', u'authorUrl': u'https://ieeexplore.ieee.org/author/37349004200', u'full_name': u'S. A. Belbas', u'id': 37349004200}] 1989 American Control Conference, 1989

We extend the dynamic programming method to certain systems parametrized by two-dimensional time. Using dynamic programming arguments, we obtain a maximum principle analogous to the classical principle of Pontryagin.


An 8Kb content-addressable and reentrant memory

[{u'author_order': 1, u'affiliation': u'Matsushita Electric Industrial Co., Ltd., Osaka, Japan', u'full_name': u'H. Kodata'}, {u'author_order': 2, u'full_name': u'J. Miyake'}, {u'author_order': 3, u'full_name': u'Y. Nishimichi'}, {u'author_order': 4, u'full_name': u'H. Kudo'}, {u'author_order': 5, u'full_name': u'K. Kagawa'}] 1985 IEEE International Solid-State Circuits Conference. Digest of Technical Papers, 1985

A30μ × 36μmemory cell implemented in 2μ CMOS affording a 100ns cycle time will be described.


Heterogeneous beliefs, trading volume, and seemingly emotional stock market behavior

[{u'author_order': 1, u'affiliation': u'School of Economics and Management, Tsinghua University, Beijing 100084, China', u'full_name': u'Zhanhui Chen'}, {u'author_order': 2, u'affiliation': u'School of Economics and Management, Tsinghua University, Beijing 100084, China', u'full_name': u'Xin Yang'}] Tsinghua Science and Technology, 2007

Various information types and rational learning methods have shown that heterogeneous belief changes in a rational expectation model can explain many empirical findings in stock markets, such as momentum, contrarians, and technical trading. The methods have also shown that momentum and price movements can coexist in an asset market with only rational agents. The purpose of this paper is to ...


An expert system with fuzzy sets for optimal planning

[{u'author_order': 1, u'full_name': u'A.K. Davis'}, {u'author_order': 2, u'full_name': u'Zhao Rong-da'}] IEEE Power Engineering Review, 1991

None


On the hybrid dynamic programming principle

[{u'author_order': 1, u'full_name': u'M.S. Sheikh'}] International Multi Topic Conference, 2002. Abstracts. INMIC 2002., 2002

Summary form only given, as follows. A Class of the hybrid optimal problem is formulated and a hybrid dynamic programming principle (DPP) is presented which constitutes a generalization of the celebrated dynamic programming principle of Richard Bellman. It is shown that similarly to the case of continuous dynamic programming, which leads to the well-known Hamilton-Jacobi-Bellman (HJB) functional partial differential equation, ...


More Xplore Articles

Educational Resources on Dynamic programming

Back to Top

eLearning

No eLearning Articles are currently tagged "Dynamic programming"

IEEE-USA E-Books

  • Introduction

    This chapter presents an overview of key concepts covered in this book. The book studies adaptive dynamic programming (ADP) for uncertain linear systems, of which the only a priori knowledge is an initial, stabilizing static state‐feedback control policy. Two ADP methods, on‐policy learning and off‐policy learning, are introduced to achieve online implementation of conventional policy iteration. The book then focuses on the theory of global ADP (GADP). It aims at simultaneously improving the closed‐loop system performance and achieving global asymptotic stability of the overall system at the origin. The book also applies the robust ADP (RADP) framework to solve the decentralized optimal control problem for a class of large‐scale uncertain systems, and studies sensorimotor control with static and dynamic uncertainties under the framework of RADP.

  • Semi‐Global Adaptive Dynamic Programming

    This chapter explores the adaptive dynamic programming (ADP) methods to handle affine nonlinear systems via neural network‐based approximation. An online learning method with convergence analysis is provided and it achieves semi‐global stabilization for nonlinear systems in that the domain of attraction can be made arbitrarily large, but bounded, by tuning the controller parameters or design functions. Two most frequently used techniques in reinforcement learning are value iteration and policy iteration. When the system dynamics are uncertain, the approximation can be realized using online information via reinforcement learning and ADP methods. Neural network‐based ADP methods for nonlinear control systems are being actively developed by a good number of researchers. Some recent theoretical results include ADP for non‐affine nonlinear systems, ADP for saturated control design, ADP for nonlinear games, and ADP for nonlinear tracking problems.

  • Global Adaptive Dynamic Programming for Nonlinear Polynomial Systems

    This chapter brings more advanced optimization techniques, such as semidefinite programming (SDP) and sum‐of‐squares (SOS) programming, into adaptive dynamic programming (ADP) design. The goal is to achieve adaptive suboptimal online learning and, at the same time, maintain global asymptotic stability of the closed‐loop system. The chapter considers an auxiliary optimization problem, which helps to obtain a suboptimal solution to the minimization problem, and develops a policy iteration method for polynomial systems using SOS‐based methods. The chapter also develops an online learning method based on the idea of ADP to implement the iterative scheme with real‐time data, instead of identifying the system dynamics. Finally, it extends the proposed global ADP method to deal with an enlarged class of nonlinear systems, and covers the numerical simulation for four different examples.

  • Robust Adaptive Dynamic Programming

    This chapter introduces a new concept of robust adaptive dynamic programming (RADP), a natural extension of ADP to uncertain dynamic systems. It presents an online learning strategy for the design of robust adaptive suboptimal controllers that globally asymptotically stabilize the system. The chapter introduces the robust redesign technique to achieve RADP for nonlinear systems. To begin with, it considers the nonlinear system with dynamic uncertainties. The RADP methodologies can be viewed as natural extensions of ADP to dynamically perturbed uncertain systems. The RADP framework decomposes the uncertain environment into two parts: the reduced‐order system (ideal environment) with known system order and fully accessible state, and the dynamic uncertainties, with unknown system order and dynamics, interacting with the ideal environment. The presence of dynamic uncertainty gives rise to interconnected systems for which the controller design and robustness analysis become technically challenging.

  • Adaptive Dynamic Programming for Uncertain Linear Systems

    This chapter presents a reinforcement learning‐inspired adaptive dynamic programming (ADP) approach for finding a new class of online adaptive optimal controllers for uncertain linear systems. Comparing the on‐policy and the off‐policy learning strategies, it can be seen that the former spreads the computational burden into different iteration time points, at the price of a longer learning process. The latter can achieve faster learning by making full use of the online measurements, at the expense of heavier computational efforts at a single iteration time point. The chapter uses two examples to validate, through numerical simulations, the effectiveness of the proposed algorithms. The first example is created using Simulink Version 8.5 (R2015a). It illustrates how the on‐policy learning can be applied to a third‐order linear system. The second example is implemented in MATLAB scripts. It applies the off‐policy learning strategy to design an approximate optimal feedback control policy for a diesel engine.

  • Deterministic Sequence Recognition for ASR

    This chapter contains sections titled:IntroductionIsolated Word RecognitionConnected Word RecognitionSegmental ApproachesDiscussionExercises

  • Solving the KCT Problem: Large‐Scale Neighborhood Search and Solution Merging

    This chapter contains sections titled:IntroductionHybrid Algorithms for the KCT ProblemExperimental AnalysisConclusionsReferences

  • Unit Commitment

    This chapter introduces several major techniques for solving the unit commitment (UC) problem, such as the priority method, dynamic programming, and the Lagrange relaxation method. Several new algorithms are then added to tackle UC problems. These are the evolutionary programming-based tabu search method, particle swarm optimization, and the analytic hierarchy process (AHP). The chapter provides a number of numerical examples and analyses. The classic UC problem is to minimize total operational cost and is subject to minimum up- and downtime constraints, crew constraints, unit capability limits, generation constraints, and reserve constraints. The AHP is a decision-making approach. It presents alternatives and criteria, evaluates trade-off, and performs a synthesis to arrive at a final decision. This chapter addresses future UC requirements in a deregulated environment where network constraints, reliability, value of generation, and variational changes in demands and other costs may be factors.

  • Robust Adaptive Dynamic Programming for Large‐Scale Systems

    This chapter explains the robust adaptive dynamic programming (RADP) theory for the decentralized optimal control of a generalized class of large‐scale systems. The controller design of each subsystem only utilizes local state variables, without knowing the system dynamics. By integrating a simple version of the cyclic‐small‐gain theorem, asymptotic stability can be achieved by assigning appropriate weighting matrices for each subsystem. As a by‐product, certain suboptimality properties can be obtained. The chapter describes the class of large‐scale uncertain systems to be studied. Then, an RADP‐based decentralized optimal controller design scheme is presented. It is also shown that the closed‐loop interconnected system enjoys some suboptimality properties. In addition, the effectiveness of the proposed methodology is demonstrated via its application to the online learning control of a ten‐machine power system with governor controllers.

  • Robust Adaptive Dynamic Programming as A Theory of Sensorimotor Control

    Many tasks that humans perform in our everyday life involve different sources of uncertainties. However, it is interesting and surprising to notice how the central nervous system (CNS) can gracefully coordinate our movements to deal with these uncertainties. This chapter studies sensorimotor control with static and dynamic uncertainties under the framework of robust adaptive dynamic programming (RADP). The linear version of RADP is extended for stochastic systems by taking into account signal‐dependent noise, and the proposed method is applied to study the sensorimotor control problem with both static and dynamic uncertainties. Results presented in the chapter suggest that the CNS may use RADP‐like learning strategy to coordinate movements and to achieve successful adaptation in the presence of static and/or dynamic uncertainties. In the absence of dynamic uncertainties, the learning strategy reduces to an ADP‐like mechanism.



Standards related to Dynamic programming

Back to Top

(Replaced) IEEE Standard VHDL Language Reference Manual

his standard revises and enhances the VHDL language reference manual (LRM) by including a standard C language interface specification; specifications from previously separate, but related, standards IEEE Std 1164 -1993,1 IEEE Std 1076.2 -1996, and IEEE Std 1076.3-1997; and general language enhancements in the areas of design and verification of electronic systems.



Jobs related to Dynamic programming

Back to Top