Progressive Bayesian estimation for nonlinear discrete-time systems: the measurement step

This paper is concerned with recursively estimating the internal state of a nonlinear dynamic system by processing noisy measurements and the known system input. In the case of continuous states, an exact analytic representation of the probability density characterizing the estimate is generally too complex for recursive estimation or even impossible to obtain. Hence, it is replaced by a convenient type of approximate density characterized by a finite set of parameters. Of course, parameters are desired that systematically minimize a given measure of deviation between the (often unknown) exact density and its approximation, which in general leads to a complicated optimization problem. Here, a new framework for state estimation based on progressive processing is proposed. Rather than trying to solve the original problem, it is exactly converted into a corresponding system of explicit ordinary first-order differential equations. Solving this system over a finite "time" interval yields the desired optimal density parameters.

71 - Integrable Combinations

Using the Lie symmetry approach, we have examined herein the system of partial differential equations corresponding to the Einstein–Maxwell equations for a static axially symmetric spacetime. The method used reduces the system of partial differential equations to a system of ordinary differential equations according to the Lie symmetry admitted. In particular, we found the relevant system of ordinary differential equations is all optimal subgroups. The system of ordinary differential equations is further solved in general to obtain exact solutions. Several new physically important families of exact solutions are derived.

It is well known that a model of any electrical circuit can be translated into a system of ordinary differential equations. Usually, the simulation is performed in the time-domain using classical discrete approaches convenient for both linear and non-linear systems. An other method consists in the quantization on the variable magnitude which is based on discrete events. The discrete event system specification formalism is adapted to simulate any electrical circuit described with a system of ordinary differential equations using the system state quantification. In this paper, a solution to make more efficient the magnitude quantification for a system of linear ordinary differential equations is given. The proposed technique is used to simulate a simple three phase coupled power systems and it is compared to other solutions given by the explicit integration using formal language and the time-domain discretization using fixed-step fifth order Runge-Kutta integration method.

We consider boundary value problems with periodic boundary conditions for first-order linear systems of impulsive ordinary differential equations with unknown right-hand sides and jumps of solutions at the impulse points entering into the statement of these problems which are assumed to be subjected to some quadratic restrictions. From indirect noisy observations of their solutions on a finite system of intervals, we obtain the optimal, in certain sense, estimates of images of their right-hand sides under linear continuous operators. Under the condition that the unknown correlation functions of noises in observations belong to some special sets, it is established that such estimates and estimation errors are expressed explicitly via solutions of special periodic boundary value problems for linear impulsive systems of ordinary differential equations.

3 - LINEAR SYSTEMS

This article considers bifurcation of families of periodic solutions from equilibrium states for systems of differential equations with constant delays that, upon linearization at zero, become systems of ordinary differential equations. An averaging transformation is constructed to simplify solution of the bifurcation problem.

Using the Lie symmetry approach, the authors have examined exact and numerical solutions of coupled short pulse equation with time-dependent coefficients. The method reduces the system of partial differential equations to a system of ordinary differential equations according to the Lie symmetry admitted. In particular, we found the relevant system of ordinary differential equations for all optimal subgroups. The system of ordinary differential equations is further studied in general to obtain exact and numerical solutions. Several new physically important families of exact and numerical solutions are derived.

An effective numerical technique for solving a special class of ordinary difference equations

Computer simulation of vehicular traffic on the real road network can be used to solve a whole range of relevant and practical problems. The microscopic approach and tens of thousands of vehicles used in simulation lead to large systems of ordinary differential equations. The vehicle dynamics can vary significantly. As a result, the corresponding systems of differential equations have a certain feature, i.e., the rate of change in unknown vector components, which in this case are the speeds of vehicles and the distances (gaps) between them, varies significantly. In this paper, we propose a multirate numerical integration scheme, in which an individual microstep is used for each component of the vector of unknowns within each macrostep. The values of the steps are determined using the obtained local error estimate of the given numerical scheme. The corresponding time-stepping strategy is obtained both for vehicle speeds and for distances between vehicles. Moreover, the multirate solver’s local error for gaps is estimated one order of accuracy higher than for speeds because drivers estimate the gap primarily rather than the speed. The developed numerical method shows a significant computational speedup in comparison with the corresponding single-rate method.

We study a model of the synchronous electric motor, which is described by a system of ordinary differential equations, including equations for electric currents in the windings of the rotor. The load moment is assumed to be a nonlinear function of the angular velocity of the rotor, allowing a linear estimate. The system of differential equations under consideration has a countable number of stationary solutions corresponding to the operating mode of uniform rotation of the rotor with the angular velocity equal to the angular velocity of rotation of the magnetic field in the stator. An effective sufficient condition is derived under which any motion of the rotor of the synchronous electric motor tends with time to uniform rotation.

The data-driven modeling of dynamical systems is an important scientific activity, and many studies have applied genetic programming (GP) to the task of automatically constructing such models in the form of systems of ordinary differential equations (ODEs). These previous studies assumed that data measurements were available for all variables in the system, whereas in real-world settings, it is typically the case that one or more variables are unmeasured or "hidden." Here, we investigate the prospect of automatically constructing ODE models of dynamical systems from time series data with GP in the presence of hidden variables. Several examples with both synthetic and physical systems demonstrate the unique challenges of this problem and the circumstances under which it is possible to reverse-engineer both the form and parameters of ODE models with hidden variables.

The purpose of this paper is to derive a means of scaling the dependent variables of a system of linear differential equations so that the normalized system accurately reflects the dependence of the system on initial data. The theory is developed for the special case of a system of first order hyperbolic partial differential equations in two independent variables. Several other systems of differential equations, to which the same normalization could be applied, are also indicated. The scaling is equivalent to row and column scaling commonly used in linear algebra. For the case of positive matrices (that is, for the case where the diagonalizing matrix and its inverse have no zero elements), the theory for the “1” and “$\infty $ ” norms has been thoroughly developed by Bauer [1]. The assumption of positive matrices is too stringent for some applications. Thus, the theory is generalized to show that for irreducible matrices a best scaling exists (for both the “1” and “ $\infty $” norms), while for simply reducible matrices a best scaling exists for at least one of these two norms. Several examples are given.

Numerical use of {[formula omitted]}-approximation method thermoelastic anisotropic thin shell theory equations represented in a special form

In this paper we present a technique to study the existence of rational solutions for systems of differential equations — for an ordinary differential equation, in particular. The method is relatively straightforward; it is based on a rationality characterisation that involves matrix Pade approximants. It is important to note that, when the solution is rational, we use formal power series “without taking into account” their circle of convergence; at the end of this paper we justify this. We expound the theory for systems of linear first-order ordinary differential equations in the general case. However, the main ideas are applied in numerical resolution of partial differential equations.