In this paper, the relation between synchronization and control of chaotic nodes connected through a time–varying network is discussed. In particular, the effects of pinning control on a set of moving chaotic agents are investigated showing that the role of system parameters, like agent density, is critical in order to reach the synchronous behavior and also to control the whole network by pinn...

model predictive controller is widely used in industrial plants. uncertainty is one of the critical issues in real systems. in this paper, the direct adaptive simplified model predictive control (smpc) is proposed for unknown or time varying plants with uncertainties. by estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly ca...

In this paper, we describe a new Stata command, stlh, which estimates and tests for the significance of the time-varying regression coefficients in Aalen’s linear hazards model; see Aalen (1989). We see two potential uses for this command. One may use it as an alternative to a proportional hazards or other nonlinear hazards regression model analysis to describe the effects of covariates on surv...

This paper studies the induced L2-norm problem for switched linear parameter varying (LPV) systems using a blending method. For a switched LPV system where the parameters are grouped into slow-varying and fast-varying parameters, the blending method is used to construct blended Lyapunov functions based on the multiple Lyapunov functions conditions in terms of linear matrix inequalities (LMIs). ...

Structural changes often occur in economics and finance due to preferences, technologies, institutional arrangements, policies, crises, etc. Improving forecast accuracy of economic time series with structural is a long-standing problem. Model averaging aims at providing an insurance against selecting poor model. All existing model approaches the literature are designed constant (non-time-varyin...

This article addresses estimation in regression models for longitudinally-collected functional covariates (time-varying predictor curves) with a longitudinal scaler outcome. The framework consists of estimating a time-varying coefficient function that is modeled as a linear combination of time-invariant functions with time-varying coefficients. The model uses extrinsic information to inform the...