000			06528nam a2201489 i 4500
001			5273582
003			IEEE
005			20200421114116.0
006			m o d
007			cr |n|||||||||
008			151221s2004 njua ob 001 eng d
020			_a9780470544785 _qelectronic
020			_z9780471660545 _qprint
020			_z0470544783 _qelectronic
024	7		_a10.1109/9780470544785 _2doi
035			_a(CaBNVSL)mat05273582
035			_a(IDAMS)0b000064810d1139
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aT57.83 _b.H36 2004eb
082	0	4	_a519.7/03 _222
245	0	0	_aHandbook of learning and approximate dynamic programming / _c[edited by] Jennie Si ... [et al.].
264		1	_aHoboken, New Jersey : _bIEEE Press, _cc2004.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2004]
300			_a1 PDF (xxi, 644 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aIEEE press series on computational intelligence ; _v2
504			_aIncludes bibliographical references and index.
505	0		_aForeword. -- 1. ADP: goals, opportunities and principles. -- Part I: Overview. -- 2. Reinforcement learning and its relationship to supervised learning. -- 3. Model-based adaptive critic designs. -- 4. Guidance in the use of adaptive critics for control. -- 5. Direct neural dynamic programming. -- 6. The linear programming approach to approximate dynamic programming. -- 7. Reinforcement learning in large, high-dimensional state spaces. -- 8. Hierarchical decision making. -- Part II: Technical advances. -- 9. Improved temporal difference methods with linear function approximation. -- 10. Approximate dynamic programming for high-dimensional resource allocation problems. -- 11. Hierarchical approaches to concurrency, multiagency, and partial observability. -- 12. Learning and optimization - from a system theoretic perspective. -- 13. Robust reinforcement learning using integral-quadratic constraints. -- 14. Supervised actor-critic reinforcement learning. -- 15. BPTT and DAC - a common framework for comparison. -- Part III: Applications. -- 16. Near-optimal control via reinforcement learning. -- 17. Multiobjective control problems by reinforcement learning. -- 18. Adaptive critic based neural network for control-constrained agile missile. -- 19. Applications of approximate dynamic programming in power systems control. -- 20. Robust reinforcement learning for heating, ventilation, and air conditioning control of buildings. -- 21. Helicopter flight control using direct neural dynamic programming. -- 22. Toward dynamic stochastic optimal power flow. -- 23. Control, optimization, security, and self-healing of benchmark power systems.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_a. 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.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/21/2015.
650		0	_aDynamic programming.
650		0	_aAutomatic programming (Computer science)
650		0	_aMachine learning.
650		0	_aControl theory.
650		0	_aSystems engineering.
655		0	_aElectronic books.
695			_aAdaptation model
695			_aAerospace control
695			_aAerospace electronics
695			_aAlgorithm design and analysis
695			_aAnalytical models
695			_aApproximation algorithms
695			_aApproximation methods
695			_aArgon
695			_aArtificial neural networks
695			_aAtmospheric modeling
695			_aAutomatic test pattern generation
695			_aBenchmark testing
695			_aBooks
695			_aCities and towns
695			_aCoils
695			_aCommunities
695			_aConcurrent computing
695			_aConferences
695			_aControl systems
695			_aConvergence
695			_aData structures
695			_aDecision making
695			_aDriver circuits
695			_aDynamic programming
695			_aDynamic scheduling
695			_aEigenvalues and eigenfunctions
695			_aEquations
695			_aEstimation
695			_aFocusing
695			_aFunction approximation
695			_aFuzzy control
695			_aGenerators
695			_aHelicopters
695			_aHeuristic algorithms
695			_aHidden Markov models
695			_aHistory
695			_aHumans
695			_aIndexes
695			_aLearning
695			_aLearning systems
695			_aLinear programming
695			_aLoad flow
695			_aLoss measurement
695			_aMachine learning
695			_aMachine learning algorithms
695			_aMarkov processes
695			_aMathematical model
695			_aMeasurement
695			_aMissiles
695			_aOptimal control
695			_aOptimization
695			_aPower system dynamics
695			_aPower system stability
695			_aProcess control
695			_aProgramming
695			_aProposals
695			_aPropulsion
695			_aRecurrent neural networks
695			_aResource management
695			_aRoads
695			_aRobots
695			_aRobust control
695			_aRobustness
695			_aRotors
695			_aSections
695			_aSecurity
695			_aSensitivity
695			_aStability analysis
695			_aStability criteria
695			_aState estimation
695			_aSteady-state
695			_aStochastic systems
695			_aSupervised learning
695			_aTraining
695			_aTrajectory
695			_aUncertainty
695			_aVectors
695			_aWater heating
700	1		_aSi, Jennie.
710	2		_aJohn Wiley & Sons, _epublisher.
710	2		_aIEEE Xplore (Online service), _edistributor.
776	0	8	_iPrint version: _z9780471660545
830		0	_aIEEE press series on computational intelligence ; _v2
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5273582
942			_cEBK
999			_c59568 _d59568