Cheng Han^1,*, Zhengguang Xu^1,2

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 >

Xiangquan Li^1,2, Zhengguang Xu^1,*, Cheng Han¹, Ning Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1807-1825, 2022, DOI:10.32604/cmes.2022.017799

Abstract This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm (M-AM-SGRPIA) for a class of single input single output (SISO) linear output error models with multi-threshold quantized observations. It proves the convergence of the designed algorithm. A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output (SIMO) or SISO nonlinear systems, and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system. The system input design is accomplished using the measurement technology of random repeatability test, and the probabilistic characteristic… More >