Download Adaptive stochastic optimization techniques with by James A. Momoh PDF

By James A. Momoh

Adaptive Stochastic Optimization suggestions with Applications presents a unmarried, handy resource for state of the art details on optimization innovations used to resolve issues of adaptive, dynamic, and stochastic positive factors. featuring 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 stories from real strength systems
  • Discusses the restrictions of present optimization recommendations in assembly the demanding situations of shrewdpermanent electrical grids

Adaptive Stochastic Optimization thoughts with Applications describes state-of-the-art optimization equipment used to deal with large-scale approach difficulties acceptable to energy, strength, communications, transportation, and economics.

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This method is applicable to optimization problems where the objective function is linear subject to linear inequality constraints. A logarithmic penalty function is used in the formulation of the Lagrange function of the constrained optimization problem. The method employs minimal inverse calculations to increase the speed of computation. In the past, primal–dual algorithms have emerged as the most important and useful algorithms from the IP class. Primal–dual algorithms, which include path-following algorithms (short-step, long-step, and predictor–corrector), ­potential-reduction algorithms, and infeasible-IP algorithms, can be extended to more general classes of problems such as semidefinite programming and general problems in convex programming.

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. 27. M. H. Lee, Approximate dynamic programming strategies and their applicability for process control: A review and future directions, International Journal of Control, Automation, and Systems, 2(3), 263–278, September 2004.

Powell, and D. Wunsch II, Handbook of Learning and Approximate Dynamic Programming, Hoboken, NJ: Wiley, 2004. 4. J. Werbos, The Roots of Backpropagation Form Order Derivates to Neural Networks and Political Forecasting, New York: Wiley, 1994. 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.

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