We introduce the problem of estimation of state space parameters, argue that it is an interesting and practically relevant problem, and study several simple estimation techniques. Particularly, we focus on estimation of the number of reachable states. We study techniques based on sampling of the state space and techniques that employ data mining techniques (classification trees, neural networks...

AIrstract-The delay systems considered here are represented by linear delay differential equations. The system parameters and the delays are assumed to be imperfectly known. The instantaneous state vector is perturbed by a bounded external disturbance vector. The problem addressed is that of characterizing conditions that guarantee that the trajectory of the instantaneous state vector remains i...

We propose a new approach to the pricing and hedging of contingent claims under transaction costs in a general incomplete market in discrete time. Under the assumptions of a bounded mean-variance tradeoff, substantial risk and a nondegeneracy condition on the conditional variances of asset returns, we prove the existence of a locally risk-minimizing strategy inclusive of transaction costs for e...

PS*, a new sequential tree searching algorithm based on the State Space Search (SSS*), is presented. PS*(k) divides each MAX node of a game tree into k partitions, which are then searched in sequence. By this means two major disadvantages of SSS*, storage demand and maintenance overhead, are significantly reduced, and yet the corresponding increase in nodes visited is not so great even in the r...

State-space caching is a veri cation technique for nite-state concurrent systems. It performs an exhaustive exploration of the state space of the system being checked while storing only all states of just one execution sequence plus as many other previously visited states as available memory allows. So far, this technique has been of little practical signi cance: it allows one to reduce memory ...

Kalman filter is linear optimal estimator for random signals. We develop state-space RLS that is counterpart of Kalman filter for deterministic signals i.e. there is no process noise but only observation noise. State-space RLS inherits its optimality properties from the standard least squares. It gives excellent tracking performance as compared to existing forms of RLS. A large class of signals...

although lcl filters are used widely in the grid connected inverters to reduce high-orderharmonics, such a system increases system order and therefore sustainable design of closed-loopcontroller system will be complicated. recently, the single-loop control strategy has beensuggested for l or lc filter based grid-connected inverters. however, the use of single-loopcontrol directly in lcl filter-...

In this paper we consider the problem of constructing a well-posed state space model for a class of singular integro-diierential equations of neutral type. The work is motivated by the need to develop a framework for the analysis of numerical methods for designing control laws for aeroelastic and other uid/structure systems. Semigroup theory is used to establish existence and well-posedness res...

Predictive state representations (PSRs) are powerful methods of modeling dynamical systems by representing state through observational data. Most of the current PSR techniques focus on learning a complete PSR model from the entire state space. Consequently, the techniques are often not scalable due to the dimensional curse, which limits applications of PSR. In this paper, we propose a new PSR l...

The main challenge in BDD-based verification is dealing with the memory explosion problem during reachability analysis. In this paper we advocate a methodology to handle this problem based on state space partitioning of functions as well as relations. We investigate the key questions of how to perform partitioning in reachability based verification and provide suitable algorithms. We also addre...