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Article type: Research Article
Authors: Chen, Yanfen
Affiliations: School of Mathematics and Statistics, Hanshan Normal University, Chaozhou, Guangdong 521041, China | E-mail: chenyanfen0123@126.com
Correspondence: [*] School of Mathematics and Statistics, Hanshan Normal University, Chaozhou, Guangdong 521041, China. E-mail: chenyanfen0123@126.com.
Abstract: The exploration of stochastic partial differential equations in noisy perturbations of dynamical systems remains a major challenge at this stage. The study analyzes the effective dynamical system combining degenerate additive noise-driven stochastic partial differential equations, firstly in the first class of stochastic partial differential equations, the terms in the non-nuclear space formed by nonlinear interactions are overcome by effectively replacing the elements in the non-nuclear space through the ItÔ formulation, and thus the final effective dynamical system is obtained. The effective dynamical system is then obtained in the second type of stochastic partial differential equation using the O-U process similar to the terms in the non-nuclear space. At noise disturbance amplitudes of 5%, 10%, 15% and 20% AC voltage maxima in that order, the effective dynamical systems for the first type of stochastic partial differential equation and the second type of stochastic partial differential equation are more stable compared to the other types of partial differential equation dynamical systems, with the maximum range of error rate improvement for the sampling points 0–239 voltage rms and voltage initial phase value being 3.62% and 26.85% and 2.13% and 19.86% for sampling points 240–360, respectively. The effective dynamic system and stochastic partial differential equation obtained by the research have very high approximation effect, and can be applied to mechanical devices such as thermal power machines.
Keywords: Stochastic partial differential equations, dynamical systems, noise, time scale, kernel space, ItÔ formula, O-U process
DOI: 10.3233/JCM-226914
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 5, pp. 2645-2657, 2023
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