An estimation of distribution algorithm for the computation of innovation estimators of diffusion processes

Estimation of Distribution Algorithms (EDAs) and Innovation Method are recognized methods for solving global optimization problems and for the estimation of parameters in diffusion processes, respectively. Well known is also that the quality of the Innovation Estimator strongly depends on an adequat...

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Bibliographic Details
Published in:arXiv.org 2018-04
Main Authors: Zochil González Arenas, Jimenez, Juan Carlos, Lozada-Chang, Li-Vang, Santana, Roberto
Format: Article
Language:English
Subjects:
Algorithms
Complexity
Computation
Computer simulation
Diffusion
Estimators
Feasibility studies
Global optimization
Innovations
Local optimization
Mathematical models
Nonlinear dynamics
Optimization algorithms
Parameter estimation
Process parameters
Simulation
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Summary:Estimation of Distribution Algorithms (EDAs) and Innovation Method are recognized methods for solving global optimization problems and for the estimation of parameters in diffusion processes, respectively. Well known is also that the quality of the Innovation Estimator strongly depends on an adequate selection of the initial value for the parameters when a local optimization algorithm is used in its computation. Alternatively, in this paper, we study the feasibility of a specific EDA - a continuous version of the Univariate Marginal Distribution Algorithm (UMDAc) - for the computation of the Innovation Estimators. Numerical experiments are performed for two different models with a high level of complexity. The numerical simulations show that the considered global optimization algorithms substantially improves the effectiveness of the Innovation Estimators for different types of diffusion processes with complex nonlinear and stochastic dynamics.
ISSN:2331-8422