Modeling and Analysis of Output Processes of Linear Continuous Stochastic Systems Based on Orthogonal Expansions of Random Functions

2020 ◽  
Vol 59 (3) ◽  
pp. 322-337 ◽  
Author(s):  
K. A. Rybakov
Keyword(s):  
Stochastic Systems ◽  
Orthogonal Expansions ◽  
Modeling And Analysis ◽  
Random Functions

scholarly journals Importance of statistical thinking in engineering and its relation to complex and creative thinking with complex and creative thinking

Athenea ◽  
2022 ◽  
Vol 2 (6) ◽  
pp. 22-27
Author(s):  
Luis Jose Gonzalez lugo
Keyword(s):  
Systems Theory ◽  
Creative Thinking ◽  
Stochastic Systems ◽  
Statistical Thinking ◽  
Total Quality ◽  
Modeling And Analysis ◽  
Statistical Review ◽  
Scientific Essay

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.

Modeling and Analysis of Stochastic Systems

1998 ◽  
Vol 93 (443) ◽  
pp. 1244
Author(s):  
Christos Alexopoulos ◽  
Vidyadhar G. Kulkarni
Keyword(s):  
Stochastic Systems ◽  
Modeling And Analysis

Development of algorithmic support for the analysis of stochastic systems based on canonical expansions of random functions

2011 ◽  
Vol 72 (2) ◽  
pp. 405-415
Author(s):  
I. N. Sinitsyn ◽  
V. I. Sinitsyn ◽  
E. R. Korepanov ◽  
V. V. Belousov ◽  
I. V. Sergeev
Keyword(s):  
Stochastic Systems ◽  
Random Functions ◽  
Algorithmic Support

scholarly journals Implications of Noise on Neural Correlates of Consciousness: A Computational Analysis of Stochastic Systems of Mutually Connected Processes

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 583
Author(s):  
Pavel Kraikivski
Keyword(s):  
Dynamical Systems ◽  
Stochastic Systems ◽  
System Size ◽  
Neural Correlates ◽  
Spectral Entropy ◽  
Modeling And Analysis ◽  
Neural Correlates Of Consciousness ◽  
Power Spectral ◽  
Neuronal Processes ◽  
Random Fluctuations

Random fluctuations in neuronal processes may contribute to variability in perception and increase the information capacity of neuronal networks. Various sources of random processes have been characterized in the nervous system on different levels. However, in the context of neural correlates of consciousness, the robustness of mechanisms of conscious perception against inherent noise in neural dynamical systems is poorly understood. In this paper, a stochastic model is developed to study the implications of noise on dynamical systems that mimic neural correlates of consciousness. We computed power spectral densities and spectral entropy values for dynamical systems that contain a number of mutually connected processes. Interestingly, we found that spectral entropy decreases linearly as the number of processes within the system doubles. Further, power spectral density frequencies shift to higher values as system size increases, revealing an increasing impact of negative feedback loops and regulations on the dynamics of larger systems. Overall, our stochastic modeling and analysis results reveal that large dynamical systems of mutually connected and negatively regulated processes are more robust against inherent noise than small systems.

Sign in / Sign up

Export Citation Format

Share Document