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Modern Optimization Methods for Decision Making Under Risk and Uncertainty
The book comprises original articles on topical issues of risk theory, rational decision making, statistical decisions, and control of stochastic systems. The articles are the outcome of a series international projects involving the leading scholars in the field of modern stochastic optimization and decision making. The structure of stochastic optimization solvers is described. The solvers in general implement stochastic quasi-gradient methods for optimization and identification of complex nonlinear models. These models constitute an important methodology for finding optimal decisions under risk and uncertainty. While a large part of current approaches towards optimization under uncertainty ...
Simulation and Optimization Methods in Risk and Reliability Theory
This book introduces recent advances in the area of risk estimation in complex systems. The authors study new methods of accelerated modelling, asymptotical analysis and optimal estimating. The processes are modelled using large failure trees, the methodology of fuzzy sets, bayesians, methods of stochastic optimisation, and optimal models of equipment service and control. The authors suggest applying numerical methods for analysis of super-large failure trees having large amount of multiple vertices. The methods allow finding minimal sections and reducing the amount of time necessary for such calculations. The Bayesians theory is applied under conditions of uncertainty. The methods of finding robust parameter estimates for the most commonly used classes of a priori distribution functions are suggested. As an alternative approach to stochastic methods the authors propose the algorithums of critical stats estimation for the reactor's active zone that utilise the theory of fuzzy logic.
National Union Catalog
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