Top PDF Optimal filtering for systems governed by coupled ordinary and partial differential equations

Optimal filtering for systems governed by coupled ordinary and partial differential equations

Optimal filtering for systems governed by coupled ordinary and partial differential equations

-7- In Chapter IV an optimal filter is derived for a completely general class of stochastic systems governed by coupled nonlinear ordinary and partial differential equations of either fi[r]

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Approximation of the Wigner Distribution for Dynamical Systems Governed by Differential Equations

Approximation of the Wigner Distribution for Dynamical Systems Governed by Differential Equations

The approximation method presented takes advantage of the fact that, while solutions to differential equations may be in- volved and complicated the Wigner distribution of the solu- tion may be relatively simple. In addition, the method takes advantage that in the time-frequency plane monocomponent forcing terms can be effectively approximated. Extension to multicomponent forcing terms are now being investigated. Also, we point out that of particular importance are partial differential equations such as wave equations with driving forces. We have recently presented a method for directly writ- ing the equation for the Wigner distribution corresponding to the solution of a linear partial differential equation [13]. Our aim is to also develop approximation methods for partial dif- ferential equations along the same lines as we have developed here for ordinary differential equations.
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NUMERICAL MODELING OF COUPLED PARTIAL DIFFERENTIAL EQUATIONS USING RESIDUAL ERROR FUNCTIONS

NUMERICAL MODELING OF COUPLED PARTIAL DIFFERENTIAL EQUATIONS USING RESIDUAL ERROR FUNCTIONS

The residual power series method (RPSM) is based on the Taylor series expansion and the concept of a residual error function. It is efficient and conve- nient to use since it does not require discretization or linearization. The RPSM was first developed for solving first-order fuzzy differential equations. Later, it has been successfully applied to find numerical solutions for other equations, including ordinary and partial differential equations, nonlinear systems of sin- gular initial value problems, pantograph delay differential equation, fractional differential equations, fuzzy fractional differential models [1, 2, 3, 4, 5, 12, 13, 16, 17, 18, 20, 21, 22].
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On Solving Systems of Autonomous Ordinary Differential Equations by Reduction to a Variable of an Algebra

On Solving Systems of Autonomous Ordinary Differential Equations by Reduction to a Variable of an Algebra

In general the solution is not easy to obtain since this is usually a system of coupled differential equations. There is a vast literature regarding the solution of ordinary differential equations by different means and in particular by techniques utilizing generalized analytic functions, see for instance 1–14. These include applications to the three-dimensional Stokes problem, solutions of planar elliptic vector fields with degeneracies, the Dirichlet problem, multidimensional stationary Schr ¨odinger equation, among others 3, 5, 6, 12, 13. In particular, the technique that we present is of interest for people working on vector fields with singularities. For instance, in order to gain insight into the behaviour of analytic vector fields, correct visualization of vector fields in the vicinity of their singular set is required, in the case of visualization of two-dimensional complex analytic vector fields with essential singularities the usual methods only provide partial results see 15–17, whilst the technique which we promote provides accurate and correct solutions 18, 19. These questions arise naturally in discrete and continuous dynamical systems see 20–22.
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Optimization in Transition between Two Dynamic Systems Governed by a Class of Weakly Singular Integro Differential Equations

Optimization in Transition between Two Dynamic Systems Governed by a Class of Weakly Singular Integro Differential Equations

The minimum energy problem and the associated optimal control problem have been investigated for more than half a century. The system constraints can be ordinary differential equations, partial differential equations, or functional dif- ferential equations. This study introduces a numerical method for finding the minimum energy to satisfy the general criterion that can be adjusted to minim- ize various requirements through the selection of appropriate parameters. One system constraint is the class of equations of the first kind, which originates from an aeroelasticity problem where the mathematical model consists of eight integro-differential equations [1]. In the model, the most determinate equation How to cite this paper: Chiang, S. (2019)
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Feynman Kac formula for switching diffusions: connections of systems of partial differential equations and stochastic differential equations

Feynman Kac formula for switching diffusions: connections of systems of partial differential equations and stochastic differential equations

Because of the increasing demands and complexity in modeling, analysis, and compu- tation, significant efforts have been made searching for better mathematical models in recent years. It has been well recognized that many of the systems encountered in the new era cannot be represented by the traditional ordinary differential equation and/or stochastic differential equation models alone. The states of such systems have two com- ponents, namely, state = (continuous state, discrete event state). The discrete dynamics may be used to depict a random environment or other stochastic factors that cannot be represented in the traditional differential equation models. Dynamic systems mentioned above are often referred to as hybrid systems. One of the representatives in the class of hy- brid system is a switching diffusion process. A switching diffusion process can be thought of as a number of diffusion processes coupled by a random switching process. At a first glance, these processes are seemingly similar to the well-known diffusion processes. A closer scrutiny shows that switching diffusions have very different behavior compared to traditional diffusion processes. Within the class of switching diffusion processes, when the discrete event process or the switching process depends on the continuous state, the problem becomes much more difficult; see [, ]. Because of their importance, switch- ing diffusions have drawn much attention in recent years. Many results such as smooth dependence of the initial data, recurrence, positive recurrence, ergodicity, stability, and numerical methods for solution of stochastic differential equations with switching, etc., have been obtained. Nevertheless, certain important concepts are yet fully investigated. The Feynman-Kac formula is one of such representatives.
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Optimal control of nonsmooth system governed by quasi linear elliptic equations

Optimal control of nonsmooth system governed by quasi linear elliptic equations

In this paper, we discuss a class of optimal control problems of nonsmooth systems governed by quasi-linear elliptic partial differential equations, give the existence of the problem.. T[r]

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OPTIMAL CONTROL OF SYSTEMS GOVERNED BY DELAYED-DIFFERENTIAL EQUATIONS. Joseph. Goree Hyde

OPTIMAL CONTROL OF SYSTEMS GOVERNED BY DELAYED-DIFFERENTIAL EQUATIONS. Joseph. Goree Hyde

Optimal control of systems governed by delayed-dif ferential equations is explored by using the control theory developed for. systems governed by ordinary differential equations[r]

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Solving ordinary differential equations by thedormand prince method

Solving ordinary differential equations by thedormand prince method

The main purpose of this research is to utilize the algorithm of Dormand Prince method in solving a differential equations. Then, the solution obtained is compared with the other numerical methods in term of accuracy. However, this research is limited to the scope of linear ordinary differential equations of first and second order. Initial value problem is emphasized in this research [5, 6].

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A Survey on Oscillation of Impulsive Ordinary Differential Equations

A Survey on Oscillation of Impulsive Ordinary Differential Equations

Impulsive differential equations, that is, differential equations involving impulse effect, appear as a natural description of observed evolution phenomena of several real world problems. There are many good monographs on the impulsive differential equations 1– 6. It is known that many biological phenomena, involving thresholds, bursting rhythm models in medicine and biology, optimal control models in economics, pharmacokinetics, and frequency modulates systems, do exhibit impulse effects. Let us describe the Kruger-Thiemer model 7 for drug distribution to show how impulses occur naturally. It is assumed that the drug, which is administered orally, is first dissolved into the gastrointestinal tract. The drug is then absorbed into the so-called apparent volume of distribution and finally eliminated from the system by the kidneys. Let xt and yt denote the amounts of drug at time t in the gastrointestinal tract and apparent volume of distribution, respectively, and let k 1 and k 2
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Solving Ordinary Differential Equations with Evolutionary Algorithms

Solving Ordinary Differential Equations with Evolutionary Algorithms

In this paper, we have been able to formulate the general linear second order ODE as an optimization problem, and we have also been able to solve the formulated optimization problem using the Differential Evolution algo- rithm. Numerical examples also show that the method gives better approximate solutions. Other evolutionary techniques can be exploited as well.

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Stochastic ordinary differential equations in applied and computational mathematics

Stochastic ordinary differential equations in applied and computational mathematics

In this very simple setting, the Bayesian picture is very closely related to the more traditional computational mathematics approach of forming a least-squares objective function (analogous to the log-likelihood), adding a penalty function (analogous to the log of the prior), and optimizing to find a single best parameter value (analogous to computing a point that maximizes the posterior). However, working in terms of the complete posterior density, rather than just presenting an optimal parameter and possibly computing local sensitivity around that value, has benefits when there is more than one region of likely values. Further, by sampling parameter values from the posterior, we can display a set of ‘likely’ trajectories from the model.
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An Approach to Parallel Simulation of Ordinary Differential Equations

An Approach to Parallel Simulation of Ordinary Differential Equations

Previously, the increasing complexity of systems and the corresponding increases in their computational complexity were matched by faster processor speeds that kept the simulation runtime within reasonable bounds. However, since the year 2005 processor clock speeds have largely leveled off (see Figure 1), and the increase in computing power for commercial chips has been achieved by adding processor cores that can execute in parallel rather than by increasing clock speed [6]. Exploiting this parallel processing power provided by multi-core ar- chitectures to improve the run time of a simulation requires algorithmic changes to the simulation; one has to develop parallel versions of the simulation algorithms to speed up the computation. However, developing these parallel simulation algorithms will require careful consideration of the physical CPU architecture to derive the best parallel performance.
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Modified Weibull Distribution: Ordinary Differential Equations

Modified Weibull Distribution: Ordinary Differential Equations

Abstract— Modified Weibull distribution is an appreciable improvement over the Weibull distribution. This paper explores the application of differentiation to obtain the ordinary differential equations (ODE) of the probability functions of the modified Weibull Distribution. The parameters and support that characterized the distribution inevitably determine the behavior, existence, uniqueness and solution of the ODEs. The method is recommended to be applied to other probability distributions and probability functions not considered in this paper. Computer codes and programs can be used for the implementation.
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A. System of linear ordinary differential equations of first

A. System of linear ordinary differential equations of first

It is suitable to determine the structural behaviour of the classical problem of an arbitrary curved beam element. Normally this problem is formulated in a compact energy equation form, but here the research is approached in an extended system of differential equations.

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Some problems in irregular ordinary differential equations

Some problems in irregular ordinary differential equations

prove that if there exists a regular Lagrangian flow solution of 1.1 then there is a vector field equivalent to 9 such that the ordinary differential equation ~ = 9 ~, t has non-unique s[r]

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Mean periodic functions and ordinary differential equations

Mean periodic functions and ordinary differential equations

For E E = if the ¢R, the space of all complex-valued continuous functions defined on the real line and equipped with the topology of convergence uniform on all compact subsets of R, Schw[r]

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Distributional and entire solutions of ordinary differential and functional differential equations

Distributional and entire solutions of ordinary differential and functional differential equations

On the solutions in generalized functions of some ordinary differential equations with polynomial coefficients, Dokl.. On the solutions in generalized functions of ordinary differential [r]

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On Stable Reconstruction of the Impact in the System of Ordinary Differential Equations

On Stable Reconstruction of the Impact in the System of Ordinary Differential Equations

The Upper Estimation, the Asymptotic Order of Accuracy It is known that there exist constant K20 > 0 such that the lower estimation of accuracy Dh in C[a,b] is of the form v1 h.. In view[r]

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A posteriori error estimates of mixed finite element methods for general optimal control problems governed by integro differential equations

A posteriori error estimates of mixed finite element methods for general optimal control problems governed by integro differential equations

properties for the flux functions along the Gauss lines and for the scalar functions at the Gauss points via mixed projections in [–]. Also, L ∞ -error estimates for general opti- mal control problems using mixed finite element methods are considered in [, ]. In [, ], a posteriori error estimates of mixed finite element methods for general convex opti- mal control problems are addressed. However, there does not seem to exist much work on theoretical analysis for mixed finite element approximation of optimal control problems governed by integro-differential equations in the literature.
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