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Optimization in Identification of Logical-Probabilistic Risk Models

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Abstract

Identification of logical-probabilistic risk models with groups of incompatible events is investigated by the random search method. The integral multi-extremal multi-parametric aim function of the optimization problem in investigated as a function of the number of optimizations, maximal and minimal amplitudes of increments of parameters, initial value of the aim function, choice of identical or different amplitudes of increments for different parameters, and risk distribution. An effective method for finding the global extremum in identifying a logical-probabilistic risk model within reasonable computation time is developed.

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Authors and Affiliations

  1. Russian Academy of Sciences, Institute of Machine Science Problems, St. Petersburg, Russia

    A. V. Rybakov & E. D. Solozhentsev

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  1. A. V. Rybakov
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  2. E. D. Solozhentsev
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Rybakov, A.V., Solozhentsev, E.D. Optimization in Identification of Logical-Probabilistic Risk Models. Automation and Remote Control 64, 1063–1073 (2003). https://doi.org/10.1023/A:1024726000159

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