Search results for "state space"

showing 10 items of 49 documents

Pipeline Monitoring Architecture Based on Observability and Controllability Analysis

2019

Recently many techniques with different applicability have been developed for damage detection in the pipeline. The pipeline system is designed as a distributed parameter system, where the state space of the distributed parameter system has infinite dimension. This paper is dedicated to the problem of observability as well as controllability analysis in the pipeline systems. Some theorems are presented in order to test the observability and controllability of the system. Computing the rank of the controllability and observability matrix is carried out using Matlab.

0209 industrial biotechnologyRank (linear algebra)Computer sciencePipeline (computing)020208 electrical & electronic engineering02 engineering and technologyPipeline transportControllability020901 industrial engineering & automationControl theoryDistributed parameter system0202 electrical engineering electronic engineering information engineeringState spaceObservabilityMATLABcomputercomputer.programming_language2019 IEEE International Conference on Mechatronics (ICM)
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Estimating aggregated nutrient fluxes in four Finnish rivers via Gaussian state space models

2013

Reliable estimates of the nutrient fluxes carried by rivers from land-based sources to the sea are needed for efficient abatement of marine eutrophication. Although nutrient concentrations in rivers generally display large temporal variation, sampling and analysis for nutrients, unlike flow measurements, are rarely performed on a daily basis. The infrequent data calls for ways to reliably estimate the nutrient concentrations of the missing days. Here, we use the Gaussian state space models with daily water flow as a predictor variable to predict missing nutrient concentrations for four agriculturally impacted Finnish rivers. Via simulation of Gaussian state space models, we are able to esti…

Statistics and ProbabilityHydrologyWater flowEcological ModelingGaussianPhosphorusMonte Carlo methodSampling (statistics)chemistry.chemical_elementsymbols.namesakeNutrientchemistrysymbolsState spaceEnvironmental scienceEutrophicationEnvironmetrics
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Wind Shear On-Line Identification for Unmanned Aerial Systems

2014

An algorithm to perform the on line identification of the wind shear components suitable for the UAS characteristics has been implemented. The mathematical model of aircraft and wind shear in the augmented state space has been built without any restrictive assumption on the dynamic of wind shear. Due to the severe accelerations on the aircraft induced by the strong velocity variation typical of wind shear, the wind shear effects have been modeled as external forces and moments applied on the aircraft. The identification problem addressed in this work has been solved by using the Filter error method approach. An Extended Kalman Filter has been developed to propagate state. It has been tuned …

Wind shear System Identification Unmanned Aerial SystemsEngineeringbusiness.industryCovariance matrixState vectorSettore ING-IND/03 - Meccanica Del VoloFilter (signal processing)Parameter identification problemExtended Kalman filterControl theoryRobustness (computer science)Wind shearState spacePharmacology (medical)business
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Multiscale Information Storage of Linear Long-Range Correlated Stochastic Processes

2019

Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric approach which allows one to compute information storage across multiple timescales in stochastic processes displaying both short-term dynamics and long-range correlations (LRC). Our analysis is performed in the popular framework of multiscale entropy, whereby a time series is first "coarse grained" at the chosen timescale through low-pass filtering and downsampling, and then …

Conditional entropyFOS: Computer and information sciencesComputer scienceStochastic processDynamical system01 natural sciencesMeasure (mathematics)010305 fluids & plasmasMethodology (stat.ME)Multiscale Entropy Information Theory ComplexityAutoregressive model0103 physical sciencesState space010306 general physicsRepresentation (mathematics)AlgorithmStatistics - MethodologyParametric statistics
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State Space-Vector Model of Linear Induction Motors Including Iron Losses: Part II: Model Identification and Results

2018

This is the second part of a paper, divided into two parts, dealing with the definition of a space-vector dynamic model of the linear Induction motor (LIM) taking into consideration both the dynamic end-effects and the iron losses as well as the off-line identification of its parameters. The first part has treated the theoretical framework of the model. This second part is devoted to the description of an identification technique which has been suitably developed for the estimation of the parameters of the LIM dynamic model accounting for both the dynamic end-effects and iron losses, described in the first part of the paper. Such an identification technique is strictly related to the state …

State modelLinear Induction Motor (LIM)Computer scienceSystem identificationState ModelState (functional analysis)Function (mathematics)End-effectsFinite element methodEnd-effectIdentification (information)Settore ING-INF/04 - AutomaticaControl theoryLinear induction motorState spaceSpace-vector2018 IEEE Energy Conversion Congress and Exposition (ECCE)
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Théorie de système et séries temporelles

1994

The aim of this paper is to present a different representation of state space models, (innovation state space representation) which is relatively new and apparently unknown in the economics and econometrics literature and to describe some of its properties. state space representation is a very flexible form for time series and the approach taken in this paper therefore allows a broad class of models it does not impose a priori the decomposition of data series into trend and cycle

[ MATH ] Mathematics [math]mathématiques séries temporelles innovation espace état stabilité stochastiqueséries temporellesstability stochasticmathematicsstate space[MATH] Mathematics [math]innovationmathématiquesstabilité stochastiquestatisticsespace étattime serieoperations research
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Dynamic programming for 2-D discrete linear systems

1989

The authors calculate the optimal control of 2-D discrete linear systems using a dynamic programming method. It is assumed that the system is described with Roesser's state-space equations for which a 2-D sequence of inputs minimizing the given performance criterion is calculated. The method is particularly suitable for problems with bounded states and controls, although it can also be applied for unbounded cases. One numerical example is given. >

Dynamic programmingDiscrete systemSequenceControl and Systems EngineeringControl theoryBounded functionLinear systemState spaceElectrical and Electronic EngineeringMultidimensional systemsOptimal controlComputer Science ApplicationsMathematicsIEEE Transactions on Automatic Control
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Stability analysis of Beck's column over a fractional-order hereditary foundation

2018

This paper considers the case of Beck's column resting on a hereditary bed of independent springpots. The springpot possesses an intermediate rheological behaviour among linear spring and linear dashpot. It is defined by means of couple ( C β ,  β ) that characterize the material of the element and is ruled by a Caputo's fractional derivative. In this paper, we investigate the critical load of the column under the action of a follower load by means of a novel complex transform that allows to use the Routh–Hurwitz theorem in the complex half-plane for the stability analysis.

General MathematicsMathematical analysisGeneral EngineeringGeneral Physics and Astronomy02 engineering and technologyFractional calculu01 natural sciencesStability (probability)010305 fluids & plasmasFractional calculusPhysics and Astronomy (all)020303 mechanical engineering & transportsEngineering (all)0203 mechanical engineering0103 physical sciencesFollower forceRouth–Hurwitz criterionOrder (group theory)Mathematics (all)State space approachSettore ICAR/08 - Scienza Delle CostruzioniColumn (data store)Research ArticlesMathematics
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Quantitative ergodicity for some switched dynamical systems

2012

International audience; We provide quantitative bounds for the long time behavior of a class of Piecewise Deterministic Markov Processes with state space Rd × E where E is a finite set. The continuous component evolves according to a smooth vector field that switches at the jump times of the discrete coordinate. The jump rates may depend on the whole position of the process. Under regularity assumptions on the jump rates and stability conditions for the vector fields we provide explicit exponential upper bounds for the convergence to equilibrium in terms of Wasserstein distances. As an example, we obtain convergence results for a stochastic version of the Morris-Lecar model of neurobiology.

Statistics and ProbabilitySwitched dynamical systemsDynamical systems theoryMarkov process01 natural sciences34D2393E15010104 statistics & probabilitysymbols.namesakeCouplingPiecewise Deterministic Markov ProcessPosition (vector)60J25FOS: MathematicsState spaceApplied mathematicsWasserstein distance0101 mathematicsMathematicsProbability (math.PR)010102 general mathematicsErgodicityErgodicity[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Linear Differential EquationsPiecewisesymbolsJumpAMS-MSC. 60J75; 60J25; 93E15; 34D23Vector fieldStatistics Probability and Uncertainty60J75[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - Probability
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Least-squares temporal difference learning based on an extreme learning machine

2014

Abstract Reinforcement learning (RL) is a general class of algorithms for solving decision-making problems, which are usually modeled using the Markov decision process (MDP) framework. RL can find exact solutions only when the MDP state space is discrete and small enough. Due to the fact that many real-world problems are described by continuous variables, approximation is essential in practical applications of RL. This paper is focused on learning the value function of a fixed policy in continuous MPDs. This is an important subproblem of several RL algorithms. We propose a least-squares temporal difference (LSTD) algorithm based on the extreme learning machine. LSTD is typically combined wi…

Mathematical optimizationArtificial neural networkArtificial IntelligenceCognitive NeuroscienceBellman equationReinforcement learningState spaceMarkov decision processTemporal difference learningComputer Science ApplicationsMathematicsExtreme learning machineCurse of dimensionalityNeurocomputing
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