This paper proposes a method of identi cation for a discrete-time state-space model set presuming the existence of robust performance control. The model set is described by a nominal model and a parametric uncertainty bound measured with the Frobenius norm. The identi cation problem considered in this paper is to nd a model set which satis es conditions: (i) the model set is consistent with giv...

State-space models have been increasingly used to study macroeconomic and financial problems. A state-space representation consists of two equations, a measurement equation which links the observed variables to unobserved state variables and a transition equation describing the dynamics of the state variables. In this paper, we show that a classic linear-quadratic macroeconomic framework which ...

Kalman Filtering problems often have inherent and known constraints in the physical dynamics that are not exploited despite potentially significant gains (e.g., fixed speed of a motor). In this paper, we review existing methods and propose some new ideas for filtering in the presence of equality constraints. We then show that three methods for incorporating state space equality constraints are ...

We consider a class of queueing systems that consist of server pools in parallel and multiple customer classes. Customer service times are assumed to be exponentially distributed. We study the asymptotic behavior of these queueing systems in a heavy traffic regime that is known as the Halfin and Whitt many-server asymptotic regime. Our main contribution is a general framework for establishing s...

In this paper, we present an adaptation-guided similarity metric based on the estimate of the number of actions between states, called ADG (Action Distance-Guided). It is determined by using a heuristic calculation extracted from the heuristic search planning, called FF, which was the fastest planner in the AIPS’2000 competition. This heuristic provides an accurate estimate of the distance betw...

Dynamic PET reconstruction is a challenging issue due to the spatio-temporal nature and the complexity of the data. Conventional frame-by-frame approaches fail to explore the temporal information of dynamic PET data, and may lead to inaccurate results due to the low SNR of data. Due to the ill-conditioning of image reconstruction, proper prior knowledge should be incorporated to constrain the r...

For oscillating time series, the prediction is often focused on the turning points. In order to predict the turning point magnitudes and times it is proposed to form the state space reconstruction only from the turning points and modify the local (nearest-neighbor) model accordingly. The model on turning points gives optimal predictions at a lower dimensional state space than the optimal local ...