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Journal Neurocomputers №9 for 2015 г.
Article in number:
Results of a simulation experiment for Markov processes identification methods comparison
Keywords:
Markov models
models identification
multivariate non-linear optimization
simulation experiment
trained struc-tures
Authors:
P.A. Marmalyuk- Ph.D. (Eng.), Head of the Laboratory of Mathematical Psychology and Applied Software, Moscow State University of Psychology and Education. E-mail: pavel.marmalyuk@gmail.com
G.A. Yuryev - Ph.D. (Phys.-Math.), Vice Dean of Academic Affairs of the «Information technologies» Faculty, Moscow State University of Psychology and Education. E-mail: nezdeshni@gmail.com
L.S. Kuravsky - Dr. Sc. (Eng.), Professor, Dean of the «Information technologies» Faculty,
Moscow State University of Psychology and Education. E-mail: l.s.kuravsky@gmail.com
P.N. Dumin - Programmer at the Laboratory of Mathematical Psychology and Applied Software,
Moscow State University of Psychology and Education. E-mail: dumin.pn@gmail.com
Abstract:
A numerical technique for the identification of discrete-state continuous-time Markov models has been developed and described in paper [6]. The given method is characterized by utilizing both initial approximations derived from the observed data and sensi-tivity estimates of the minimized criterion to small variations of identified parameters.
Proposed approach is experimentally compared with a first order gradient method with adaptive steps [1], which is traditionally applied for solving multivariate nonlinear optimization problems. In order to assess computational efficiency of suggested algo-rithms a set of models was generated, which are structurally similar to models commonly used in practice.
Essential structure of these models corresponds to the non-homogeneous birth-death process. Details of data generation stage are presented in Section 1 whereas specifications of a computing machine used in the experiment are listed in Section 2.
Results of the computational experiments with algorithm-specific settings presented in Section 3 are described in Section 4. The results obtained demonstrate that, in the considered range of numbers of identified parameters, the developed modified identifi-cation method provides substantial speedup compared to the classical gradient method.
Pages: 44-50
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