Adaptive stochastic optimization techniques with by James A. Momoh

By James A. Momoh

Adaptive Stochastic Optimization innovations with Applications presents a unmarried, handy resource for cutting-edge details on optimization options used to unravel issues of adaptive, dynamic, and stochastic beneficial properties. proposing smooth advances in static and dynamic optimization, selection research, clever platforms, evolutionary programming, heuristic optimization, stochastic and adaptive dynamic programming, and adaptive critics, this book:

  • Evaluates optimization tools for dealing with operational making plans, Voltage/VAr, keep an eye on coordination, vulnerability, reliability, resilience, and reconfiguration issues
  • Includes mathematical formulations, algorithms for implementation, illustrative engineering examples, and case reviews from genuine energy systems
  • Discusses the restrictions of present optimization ideas in assembly the demanding situations of shrewdpermanent electrical grids

Adaptive Stochastic Optimization concepts with Applications describes state of the art optimization tools used to deal with large-scale method difficulties appropriate to energy, strength, communications, transportation, and economics.

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20. K. Warwick, A. Ekwue, and R. : IEE, 1997. C. M. H. : Elsevier Applied Science, 1987. © 2016 by Taylor & Francis Group, LLC 8 Adaptive Stochastic Optimization Techniques with Applications 22. P. Wakker, Additive Representations of Preferences: A New Foundation of Decision Analysis, Dordrecht, the Netherlands: Kluwer Academic, 1989. 23. L. P. : Pergamon Press, 1985. 24. G. Hingorani and L. Gyugyi, Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems, New York: IEEE Press, 2000.

P. Wakker, Additive Representations of Preferences: A New Foundation of Decision Analysis, Dordrecht, the Netherlands: Kluwer Academic, 1989. 23. L. P. : Pergamon Press, 1985. 24. G. Hingorani and L. Gyugyi, Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems, New York: IEEE Press, 2000. D. B. Whinston, Advances in Artificial Intelligence in Economics, Finance, and Management, vol. 1, Greenwich, CT: Jai Press, 1994. 26. C. Crouch and R. Wilson, Risk/Benefit Analysis, Cambridge, MA: Ballinger, 1982.

5. P. Werbos, ADP: Goals, opportunities, and principles, in J. G. B. Powell, and D. , Handbook of Learning and Approximate Dynamic Programming, Hoboken, NJ: John Wiley & Sons, 2004. 6. J. A. White and D. , Handbook of Intelligent Control, pp. 493–525, New York: Van Nostrand Reinhold, 1992. J. A. White and D. , Handbook of Intelligent Control, pp. 65–89, New York: Van Nostrand Reinhold, 1992. 8. H. W. Sze, Optimization in Systems Engineering, Scranton, PA: Intext, 1972. 9. P. C. , Englewood Cliffs, NJ: Prentice-Hall, 1977.

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