Constrained State-space Model Predictive Control is presented in the paper. Predictive controller based on incremental linear state-space process model and quadratic criterion is derived. Typical types of constraints are considered – limits on manipulated, state and controlled variables. Control experiments with nonlinear model of multivariable laboratory process are simulated first and real ex...

In a model-based testing approach as well as for the verification of properties, B models provide an interesting solution. However, for industrial applications, the size of their state space often makes them hard to handle. To reduce the amount of states, an abstraction function can be used, often combining state variable elimination and domain abstractions of the remaining variables. This pape...

This paper considers computational aspects of the problem of elimination of variables. More precisely, the problem of elimination of latent variables in models is addressed in the behavioral framework. In earlier contributions on this theme, the model classes of infinitely smooth C and square integrable L2 linear time-invariant systems have been considered. For both system classes, conditions f...

This paper considers a state space model with integrated latent variables. The model provides an effective framework to specify, identify and extract common stochastic trends for a set of integrated time series. The model can be readily estimated by the standard Kalman filter, whose asymptotics are fully developed in the paper. In particular, we establish the consistency and asymptotic mixed no...

Motivated by the applications of parallel and distributed computing in m−D systems, this paper analyzes the effect of time-variant communication delays on the stability of 2−D systems. The timevariant communication delays are at £rst modeled and then used in the formulation of the 2−D Roesser local state space model. The arising overall dynamic system model is of uncertain, shift-variant form a...

In a previous paper (Environ. Ecol. Stat. 5 (1998) 29.) we presented an entropy-based approach to spatial sampling design in a state-space model framework. We now address the problem of sensitivity of optimal designs with respect to the configuration of the set of potential observation sites considered, as well as to the model specifications. The latter involve both the spatial dependence struc...

In this paper a system that transforms speech waveforms to animated faces are proposed. The system relies on continuous state space models to perform the mapping, this makes it possible to ensure video with no sudden jumps and allows continuous control of the parameters in ’face space’. The performance of the system is critically dependent on the number of hidden variables, with too few variabl...

Reinforcement learning is concerned with learning to interact with environments that are initially unknown. State-of-the-art reinforcement learning approaches, such as DQN, are model-free and learn to act effectively across a wide range of environments such as Atari games, but require huge amounts of data. Modelbased techniques are more data-efficient, but need to acquire explicit knowledge abo...

We present an automatic iterative abstraction-refinement methodology in which the initial abstract model is generated by an automatic analysis of the control structures in the program to be verified. Abstract models may admit erroneous (or “spurious”) counterexamples. We devise new symbolic techniques which analyze such counterexamples and refine the abstract model correspondingly. The refineme...

State-space or latent-variable models for stock prices specify a process for expected returns and expected and unexpected dividend growth, and then derive dividend yields and returns from a present value relations. They are a useful structure for understanding and interpreting forecasting relations. In this note, I connect state-space representations with their observable counterparts, and VAR/...