A new method for the nonlinear approximation of signals. I. The optimal damping factor
A new method for the nonlinear approximation of signals. II. The convergence problem
A simultaneous localization and tracking method for a worm tracking system
The idea of worm tracking refers to the path analysis of Caenorhabditis elegans nematodes and is an important tool in neurobiology which helps to describe their behavior. Knowledge about nematode behavior can be applied as a model to study the physiological addiction process or other nervous system processes in animals and humans. Tracking is performed by using a special manipulator positioning a microscope with a camera over a dish with an observed individual. In the paper, the accuracy of a nematode's...
A stochastic functional equation. (Short Communication).
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...
Algorithms for determining the model structure of a controlled system
Applications of sharp large deviations estimates to optimal cooling schedules
Approximation approach for nonlinear filtering problem with time dependent noises. I. Conditionally optimal filter in the minimum mean square sense
Approximation approach for nonlinear filtering problem with time dependent noises. II. Stable nonlinear filters
Conditioning and error estimates in LQG design
Control of uncertain processes: applied theory and algorithms
Data-driven models for fault detection using kernel PCA: A water distribution system case study
Kernel Principal Component Analysis (KPCA), an example of machine learning, can be considered a non-linear extension of the PCA method. While various applications of KPCA are known, this paper explores the possibility to use it for building a data-driven model of a non-linear system-the water distribution system of the Chojnice town (Poland). This model is utilised for fault detection with the emphasis on water leakage detection. A systematic description of the system's framework is followed by...
Design of linear quadratic adaptive control: theory and algorithms for practice
Efficiency of the Stochastic Approximation Method
Epoch-incremental reinforcement learning algorithms
In this article, a new class of the epoch-incremental reinforcement learning algorithm is proposed. In the incremental mode, the fundamental TD(0) or TD(λ) algorithm is performed and an environment model is created. In the epoch mode, on the basis of the environment model, the distances of past-active states to the terminal state are computed. These distances and the reinforcement terminal state signal are used to improve the agent policy.
Ergodic control of Markov processes with mixed observation structure [Book]
Estimation of feedwater heater parameters based on a grey-box approach
The first-principle modeling of a feedwater heater operating in a coal-fired power unit is presented, along with a theoretical discussion concerning its structural simplifications, parameter estimation, and dynamical validation. The model is a part of the component library of modeling environments, called the Virtual Power Plant (VPP). The main purpose of the VPP is simulation of power generation installations intended for early warning diagnostic applications. The model was developed in the Matlab/Simulink...
Hybrid search for optimum in a small implicitly defined region
Identification with interruptions as an anti-bursting device
Iterative estimators of parameters in linear models with partially variant coefficients
A new kind of linear model with partially variant coefficients is proposed and a series of iterative algorithms are introduced and verified. The new generalized linear model includes the ordinary linear regression model as a special case. The iterative algorithms efficiently overcome some difficulties in computation with multidimensional inputs and incessantly appending parameters. An important application is described at the end of this article, which shows that this new model is reasonable and...