000 | 06509nam a2201477 i 4500 | ||
---|---|---|---|
001 | 5273582 | ||
003 | IEEE | ||
005 | 20191218152118.0 | ||
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 _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5273582 |
999 |
_c42164 _d42164 |