A square root filter for real time multivariate regression
A stochastic control problem.
A stochastic functional equation. (Short Communication).
A stochastic impulse control problem with quadratic growth Hamiltonian and the corresponding quasi variational inequality.
A stopping rule for discounted Markov decision processes with finite action sets
In a Discounted Markov Decision Process (DMDP) with finite action sets the Value Iteration Algorithm, under suitable conditions, leads to an optimal policy in a finite number of steps. Determining an upper bound on the necessary number of steps till gaining convergence is an issue of great theoretical and practical interest as it would provide a computationally feasible stopping rule for value iteration as an algorithm for finding an optimal policy. In this paper we find such a bound depending only...
A system of non-linear functional differential equations arising in an equilibrium model of an economy with borrowing constraints
A unified approach to multivariable discrete-time filtering based on the Wiener theory
A vanishing discount limit theorem for controlled Markov chains
A view on filtering of continuous data signals
A weak convergence approach to hybrid LQG problems with infinite control weights.
Abhandlungen zur Methode der kleinsten Quadrate [Book]
About the linear-quadratic regulator problem under a fractional brownian perturbation
In this paper we solve the basic fractional analogue of the classical linear-quadratic gaussian regulator problem in continuous time. For a completely observable controlled linear system driven by a fractional brownian motion, we describe explicitely the optimal control policy which minimizes a quadratic performance criterion.
About the linear-quadratic regulator problem under a fractional Brownian perturbation
In this paper we solve the basic fractional analogue of the classical linear-quadratic Gaussian regulator problem in continuous time. For a completely observable controlled linear system driven by a fractional Brownian motion, we describe explicitely the optimal control policy which minimizes a quadratic performance criterion.
Actions on environment under uncertainty: stochastic formulation and the associated deterministic problem.
Active fault diagnosis based on stochastic tests
The focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output or an error output from the system. The classical cumulative sum (CUSUM) test will be modified with respect to the AFD approach applied. The CUSUM method will be altered such that it will be able to detect a change...
Adaptive algorithm for noisy autoregressive signals.
Adaptive control for a jump linear system with quadratic cost
Adaptive control for discrete-time Markov processes with unbounded costs: Discounted criterion
We study the adaptive control problem for discrete-time Markov control processes with Borel state and action spaces and possibly unbounded one-stage costs. The processes are given by recurrent equations with i.i.d. -valued random vectors whose density is unknown. Assuming observability of we propose the procedure of statistical estimation of that allows us to prove discounted asymptotic optimality of two types of adaptive policies used early for the processes with bounded costs.
Adaptive control of discrete time Markov processes by the large deviations method
Some discrete time controlled Markov processes in a locally compact metric space whose transition operators depend on an unknown parameter are described. The adaptive controls are constructed using the large deviations of empirical distributions which are uniform in the parameter that takes values in a compact set. The adaptive procedure uses a finite family of continuous, almost optimal controls. Using the large deviations property it is shown that an adaptive control which is a fixed almost optimal...
Adaptive control scheme based on the least squares support vector machine network
Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine...