Search results for " Probability density function."

showing 10 items of 12 documents

Laplace’s Method of Integration in the Path Integral Approach for the Probabilistic Response of Nonlinear Systems

2020

In this paper the response of nonlinear systems under stationary Gaussian white noise excitation is studied. The Path Integral (PI) approach, generally employed for evaluating the response Probability Density Function (PDF) of systems in short time steps based on the Chapman-Kolmogorov equation, is here used in conjunction with the Laplace’s method of integration. This yields an approximate analytical solution of the integral involved in the Chapman-Kolmogorov equation. Further, in this manner the repetitive integrations, generally required in the conventional numerical implementation of the procedure, can be circumvented. Application to a nonlinear system is considered, and pertinent compa…

Nonlinear systemPath Integral Laplace’s method Nonstationary response Probability density function.Laplace transformLaplace's methodPath integral formulationProbabilistic logicApplied mathematicsProbability density functionWhite noiseSettore ICAR/08 - Scienza Delle CostruzioniExcitationMathematics
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Non-Gaussian probability density function of SDOF linear structures under wind actions

1998

Abstract Wind velocity is usually analytically described adding a static mean term to a zero mean fluctuation stationary process. The corresponding aerodynamic alongwind force acting on a single degree of freedom (SDOF) structure can be considered as a sum of three terms proportional to the mean wind velocity, to the product between mean and fluctuating part of the wind velocity and to the square power of the fluctuating wind velocity, respectively. The latter term, often neglected in the literature, is responsible for the non-Gaussian behaviour of the response. In this paper a method for the evaluation of the stationary probability density function of SDOF structures subjected to non-Gauss…

Stationary processStationary distributionSeries (mathematics)Renewable Energy Sustainability and the EnvironmentMechanical EngineeringGaussianMathematical analysisProbability density functionWind speedAerodynamic forcesymbols.namesakesymbolsSeries expansionCivil and Structural EngineeringMathematicsAlongwind response; Probability density function; Non-Gaussian stochastic analysis
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Quick and Slow Components of the Hydrologic Response at the Hillslope Scale

2016

It is widely recognized that the Hortonian mechanism of runoff generation occurs in arid and semi-arid regions, generally characterized by high rainfall intensity on soils exhibiting low infiltrabilities. Differently, in steeply sloping forested watersheds in humid climates, by infiltrating through a highly permeable upper soil horizon, water moves beneath the soil surface determining a slow response. However, in most real cases, for example when in arid regions mountain forested areas take place, both (quick and slow) runoff generation processes coexist and together contribute to the hydrologic hillslope response. In this paper, based on analytical solutions of the hydrologic response, ins…

HydrologySubsurface stormflowGamma probability density function0208 environmental biotechnologyhillslope scale overland flow subsurface stormflow instantaneous response function gamma probability density function02 engineering and technologySoil surfaceAgricultural and Biological Sciences (miscellaneous)Arid020801 environmental engineeringInstantaneous response functionOverland flowSoil waterEnvironmental scienceSoil horizonSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliSlow responseHillslope scaleScale (map)Surface runoffIntensity (heat transfer)Water Science and TechnologyCivil and Structural Engineering
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Introducing randomness in the analysis of chemical reactions: An analysis based on random differential equations and probability density functions

2021

[EN] In this work we consider a particular randomized kinetic model for reaction-deactivation of hydrogen peroxide decomposition. We apply the Random Variable Transformation technique to obtain the first probability density function of the solution stochastic process under general conditions. From the rst probability density function, we can obtain fundamental statistical information, such as the mean and the variance of the solution, at every instant time. The transformation considered in the application of the Random Variable Transformation technique is not unique. Then, the first probability density function can take different expressions, although essentially equivalent in terms of comp…

Differential equationComputational MechanicsRandom modelProbability density functionChemical reactionComputational MathematicsComputational Theory and MathematicsChemical kinetic modelRandom modelRandom variable transformation techniqueFirst probability density functionStatistical physicsMATEMATICA APLICADARandomnessMathematicsComputational and Mathematical Methods
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Probabilistic characterization of nonlinear systems under Poisson white noise via complex fractional moments

2014

In this paper, the probabilistic characterization of a nonlinear system enforced by Poissonian white noise in terms of complex fractional moments (CFMs) is presented. The main advantage in using such quantities, instead of the integer moments, relies on the fact that, through the CFMs the probability density function (PDF) is restituted in the whole domain. In fact, the inverse Mellin transform returns the PDF by performing integration along the imaginary axis of the Mellin transform, while the real part remains fixed. This ensures that the PDF is restituted in the whole range with exception of the value in zero, in which singularities appear. It is shown that using Mellin transform theorem…

Mellin transformApplied MathematicsMechanical EngineeringMonte Carlo methodMathematical analysisProbabilistic logicAerospace EngineeringOcean EngineeringProbability density functionWhite noiseComplex fractional moment Kolmogorov-Feller Mellin transform Poisson white noise Probability density functionNonlinear systemLinear differential equationControl and Systems EngineeringMellin inversion theoremElectrical and Electronic EngineeringMathematics
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Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardio-Respiratory Nonstationary Dynamics

2017

Objective: Measures of transfer entropy (TE) quantify the direction and strength of coupling between two complex systems. Standard approaches assume stationarity of the observations, and therefore are unable to track time-varying changes in nonlinear information transfer with high temporal resolution. In this study, we aim to define and validate novel instantaneous measures of TE to provide an improved assessment of complex nonstationary cardiorespiratory interactions. Methods: We here propose a novel instantaneous point-process TE (ipTE) and validate its assessment as applied to cardiovascular and cardiorespiratory dynamics. In particular, heartbeat and respiratory dynamics are characteriz…

AdultMaleInformation transferHistoryHeartbeatDatabases FactualPhysiologyEntropy0206 medical engineeringComplex systemBiomedical EngineeringHeart Rate VariabilityProbability density function02 engineering and technology01 natural sciencesPoint processStatistics NonparametricElectrocardiographyYoung Adult0103 physical sciencesProbability density functionEntropy (information theory)HumansStatistical physicsTransfer Entropy010306 general physicsBiomedical measurementMathematicsbusiness.industryHemodynamicsModels CardiovascularHeart beatSignal Processing Computer-AssistedComplexityBaroreflex020601 biomedical engineeringKolmogorov-Smirnov DistanceRespiratory Sinus ArrhythmiaBaroreflex; Biomedical measurement; Complexity; Entropy; Heart beat; Heart rate variability; Heart Rate Variability; History; Kolmogorov-Smirnov Distance; Physiology; Point Process; Probability density function; Respiratory Sinus Arrhythmia; Transfer Entropy; Biomedical EngineeringDiscrete time and continuous timePoint ProceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaPoint ProcessTransfer entropyFemaleArtificial intelligencebusiness
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On the use of fractional calculus for the probabilistic characterization of random variables

2009

In this paper, the classical problem of the probabilistic characterization of a random variable is re-examined. A random variable is usually described by the probability density function (PDF) or by its Fourier transform, namely the characteristic function (CF). The CF can be further expressed by a Taylor series involving the moments of the random variable. However, in some circumstances, the moments do not exist and the Taylor expansion of the CF is useless. This happens for example in the case of $\alpha$--stable random variables. Here, the problem of representing the CF or the PDF of random variables (r.vs) is examined by introducing fractional calculus. Two very remarkable results are o…

Characteristic function (probability theory)FOS: Physical sciencesAerospace EngineeringMathematics - Statistics TheoryOcean EngineeringProbability density functionComplex order momentStatistics Theory (math.ST)Fractional calculusymbols.namesakeIngenieurwissenschaftenFOS: MathematicsTaylor seriesApplied mathematicsCharacteristic function serieMathematical PhysicsCivil and Structural EngineeringMathematicsGeneralized Taylor serieMechanical EngineeringStatistical and Nonlinear PhysicsProbability and statisticsMathematical Physics (math-ph)Condensed Matter PhysicsFractional calculusFourier transformNuclear Energy and EngineeringPhysics - Data Analysis Statistics and ProbabilitysymbolsFractional calculus; Generalized Taylor series; Complex order moments; Fractional moments; Characteristic function series; Probability density function seriesddc:620Series expansionFractional momentProbability density function seriesSettore ICAR/08 - Scienza Delle CostruzioniRandom variableData Analysis Statistics and Probability (physics.data-an)
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Stationary and non-stationary probability density function for non-linear oscillators

1997

A method for the evaluation of the stationary and non-stationary probability density function of non-linear oscillators subjected to random input is presented. The method requires the approximation of the probability density function of the response in terms of C-type Gram-Charlier series expansion. By applying the weighted residual method, the Fokker-Planck equation is reduced to a system of non-linear first order ordinary differential equations, where the unknowns are the coefficients of the series expansion. Furthermore, the relationships between the A-type and C-type Gram-Charlier series coefficient are derived.

Stationary distributionCharacteristic function (probability theory)Applied MathematicsMechanical EngineeringMathematical analysisProbability density functionStationary sequencestochastic non-linear dynamics; Gram-Charlier expansions; approximate probability density functionGram-Charlier expansionsMechanics of Materialsstochastic non-linear dynamicsProbability distributionProbability-generating functionapproximate probability density functionSeries expansionRandom variableMathematics
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Solving fully randomized higher-order linear control differential equations: Application to study the dynamics of an oscillator

2021

[EN] In this work, we consider control problems represented by a linear differential equation assuming that all the coefficients are random variables and with an additive control that is a stochastic process. Specifically, we will work with controllable problems in which the initial condition and the final target are random variables. The probability density function of the solution and the control has been calculated. The theoretical results have been applied to study, from a probabilistic standpoint, a damped oscillator.

Differential equationDynamics (mechanics)Computational MechanicsRandom damped linear oscillatorsRandom control differential equationComputational MathematicsComputational Theory and MathematicsRandom variable transformation techniqueApplied mathematicsOrder (group theory)First probability density functionMATEMATICA APLICADALinear controlMathematics
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Anomalous diffusion and nonlinear relaxation phenomena in stochastic models of interdisciplinary physics

2020

The study of nonlinear dynamical systems in the presence of both Gaussian and non-Gaussian noise sources is the topic of this research work. In particular, after shortly present new theoretical results for statistical characteristics in the framework of Markovian theory, we analyse four different physical systems in the presence of Levy noise source. (a) The residence time problem of a particle subject to a non-Gaussian noise source in arbitrary potential profile was analyzed and the exact analytical results for the statistical characteristics of the residence time for anomalous diffusion in the form of Levy flights in fully unstable potential profile was obtained. Noise enhanced stability …

Steady-state probability density function (PDF)Settore FIS/02 - Fisica Teorica Modelli E Metodi MatematiciIdeal Chua memristorMemory devicesAnomalous diffusionLevy flightsBarrier crossing eventCorrelation time
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