Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward fit. In this article, we modify the stochastic volatility in mean (SVM) introducing state-of-the-art techniques allow for time variation degree of shrinkage. Using real-time inflation forecast exercise, sho...

Model Predictive Control (MPC) is able to directly deal with dead-time (DT) phenomena. Yet, implicit delay compensation heavily affects computational aspects of these algorithms, and stability feasibility analyses become numerically tougher. The Linear Parameter Varying (LPV) framework has been shown suitable model complex, nonlinear dynamics, corresponding MPC algorithms being developed over t...

this article presents a fuzzy robust mixed - sensitivity gain - scheduled h controller based on the loop -shaping methodology for a class of mimo uncertain nonlinear time - varying systems. in order to design this controller, the nonlinear parameter - dependent plant is first modeled as a set of linear subsystems by takagi and sugeno’s (t - s) fuzzy approach. both loop - shaping methodology and...

An available bit rate (ABR) service allows applications to access a time-varying network capacity. In a basic ABR service, the available network capacity is divided \fairly" amongst active connections, without regard to the utility that each application derives from the bandwidth allocation. The goal of this paper is to improve both the global and individual utility obtained by applications usi...

Many exist studies always use Markov decision processes (MDPs) in modeling optimal route choice in stochastic, time-varying networks. However, taking many variable traffic data and transforming them into optimal route decision is a computational challenge by employing MDPs in real transportation networks. In this paper we model finite horizon MDPs using directed hypergraphs. It is shown that th...