This paper is concerned with the state estimation problem for a class of Markovian neural networks with discrete and distributed time-delays. The neural networks have a finite number of modes, and the modes may jump from one to another according to a Markov chain. The main purpose is to estimate...

This Letter is concerned with the robust state estimation problem for uncertain time-delay Markovian jumping genetic regulatory networks (GRNs) with SUM logic, where the uncertainties enter into both the network parameters and the mode transition rate. The nonlinear functions describing the...

Estimation of pipe roughness coefficients is an important task to be carried out before any water distribution network model is used for online applications such as monitoring and control. In this study, a combined state and parameter estimation model for water distribution networks is...

This paper presents an application of a modern numerical algorithm, namely probabilistic networks, for decision support in power system analysis. The method is illustrated by a problem involving detection of small leaks in a high-pressure, fluid-filled (HPFF) pipe type cable. The construction of...

This paper describes a design for a least mean square error estimator in discrete time systems where the components of the state vector, in measurement equation, are corrupted by different multiplicative noises in addition to observation noise. We show how known results can be considered a...

Lately, several approaches to state estimation and parameter change detection based on the utilization of a finite number of process models appeared. The paper compares these methods in a unified Bayesian framework.

Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots’ state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which...

Software sensors (or state observers) are able to provide a continuous estimation of some signals (e.g. concentrations of important culture components, like biomass) which are not measured by hardware sensors. They need a mathematical model of the process and (discrete) hardware measurements of...

Renewable energy is energy that is in continuous supply over time. These kinds of energy sources are divided into five principal renewable sources of energy: the sun, the wind, flowing water, biomass and heat from within the earth. According to some studies carried out by the research...