Результаты поиска по 'parametric inference':
Найдено статей: 2
  1. Grabarnik P.Ya.
    Parameter estimation methods for random point fields with local interactions
    Computer Research and Modeling, 2016, v. 8, no. 2, pp. 323-332

    The paper gives an overview of methods for estimating the parameters of random point fields with local interaction between points. It is shown that the conventional method of the maximum pseudo-likelihood is a special case of the family of estimation methods based on the use of the auxiliary Markov process, invariant measure of which is the Gibbs point field with parameters to be estimated. A generalization of this method, resulting in estimating equation that can not be obtained by the the universal Takacs–Fiksel method, is proposed. It is shown by computer simulations that the new method enables to obtain estimates which have better quality than those by a widely used method of the maximum pseudolikelihood.

    Keywords: Gibbs point process, estimating function, pseudo-likelihood, parametric inference.
    Views (last year): 3.
  2. Vetchanin E.V., Tenenev V.A., Shaura A.S.
    Motion control of a rigid body in viscous fluid
    Computer Research and Modeling, 2013, v. 5, no. 4, pp. 659-675

    We consider the optimal motion control problem for a mobile device with an external rigid shell moving along a prescribed trajectory in a viscous fluid. The mobile robot under consideration possesses the property of self-locomotion. Self-locomotion is implemented due to back-and-forth motion of an internal material point. The optimal motion control is based on the Sugeno fuzzy inference system. An approach based on constructing decision trees using the genetic algorithm for structural and parametric synthesis has been proposed to obtain the base of fuzzy rules.

    Keywords: optimal motion control, self-locomotion, genetic algorithm, structural-parametrical synthesis, decision tree, fuzzy logic.
    Views (last year): 2. Citations: 1 (RSCI).

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