Cheng Han1,*, Zhengguang Xu1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 515-536, 2024, DOI:10.32604/cmes.2023.043464
Abstract A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems, assuming that the system satisfies the generalized Lipschitz condition. As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics, the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables, which poses difficulties in predicting and estimating the system’s output. In this article, the temporal variation of the system is described by constructing pattern category variables, which are non-deterministic variables. Since pattern category variables have… More >
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