stochastic systems
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2022 ◽  
Vol 417 ◽  
pp. 126771
Author(s):  
Lichao Feng ◽  
Qiumei Liu ◽  
Jinde Cao ◽  
Chunyan Zhang ◽  
Fawaz Alsaadi

Athenea ◽  
2022 ◽  
Vol 2 (6) ◽  
pp. 22-27
Author(s):  
Luis Jose Gonzalez lugo

Scientific essay. References [1]G. Guerrero Pino, «Determinismo, modelos y modalidades,» Revista de Filosofía, vol. XIII, nº 24, pp. 191-216, 2000. [2]V. S. Pugachev and I. N. Sinitsyn, Stochastic Systems, Theory and Applications, 2002. [3]V. G. Kulkarni, Introduction to Modeling and Analysis of Stochastic Systems, Springer, 2011. [4]R. D. Snee, «Statistical Thinking and Its Contribution to Total Quality,» The American Statistian, pp. 116-121, 1990. [5]M. Pfannkuch and C. J. Wild, «Statistical Thinking in Empirical Enquiry,» International Statistical Review, vol. 67, nº 3, pp. 223-265, 1999. [6]E. Morin, Introducción al Pensamiento Complejo, Gedisa, 1998. [7]R. Corcho, Galileo y el método científico, NATGEO CIENCIAS, 2018. [8]A. L. Arango Arias, «Aporte de Galileo a la Ciencia Moderna,» Revista Académica e Institucional de la U.C.P.R., nº 75, pp. 57-65, 2006. [9]E. Morin, El Método, Ediciones Cátedra, 2017.


Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 126
Author(s):  
Andrey Tsyganov ◽  
Julia Tsyganova

The paper considers the problem of algorithmic differentiation of information matrix difference equations for calculating the information matrix derivatives in the information Kalman filter. The equations are presented in the form of a matrix MWGS (modified weighted Gram–Schmidt) transformation. The solution is based on the usage of special methods for the algorithmic differentiation of matrix MWGS transformation of two types: forward (MWGS-LD) and backward (MWGS-UD). The main result of the work is a new MWGS-based array algorithm for computing the information matrix sensitivity equations. The algorithm is robust to machine round-off errors due to the application of the MWGS orthogonalization procedure at each step. The obtained results are applied to solve the problem of parameter identification for state-space models of discrete-time linear stochastic systems. Numerical experiments confirm the efficiency of the proposed solution.


2022 ◽  
Vol 412 ◽  
pp. 126544
Author(s):  
Qingyuan Qi ◽  
Zhenghong Qiu ◽  
Xianghua Wang ◽  
Zhijian Ji

Author(s):  
Yevgeny Somov ◽  
Nikolay Rodnishchev ◽  
Tatyana Somova

In a class of diffusion Markov processes, we formulate a problem of identification of nonlinear stochastic dynamic systems with random parameters, multiplicative and additive noises, control functions, and the state vector at a final time moment. For such systems, the identifiability conditions are being studied, and necessary conditions are formulated in terms of the general theory of extreme problems. The developed engineering methods for identification and optimizing nonlinear stochastic systems are presented as well as their application for unmanned aerial vehicles under wind disturbances caused by atmospheric turbulence, namely, for optimizing the autopilot parameters during a rotary maneuver of an unmanned aerial vehicle in translational motion, taking into account the identification of its angular velocities.


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