This paper studies a simple dynamical system of stock price fluctuation time series based on the rule of stock market. When the stock price fluctuation system is disturbed by external excitations, the system exhibits obviously chaotic phenomena, and its basic dynamic properties are analyzed. At the same time, a new fixed-time convergence theorem is proposed for achieving fixed-time control of stock price fluctuation system. Finally, the effectiveness of the method is verified by numerical simulation.

The Bayesian method is used to study the inference of the semi-parametric measurement error model (MEs) with longitudinal data. A semi-parametric Bayesian method combined with fracture prior and Gibbs sampling combined with Metropolis-Hastings (MH) algorithm is applied and applied to the simulation observation from the posterior distribution, and the combined Bayesian statistics of unknown parameters and measurement errors are obtained. We obtained Bayesian estimates of the parameters and covariates of the measurement error model. Under three different priori assumptions, four simulation studies illustrate the effectiveness and utility of the proposed method.