2,377 research outputs found
Differential-difference equations in economics: on the numerical solution of vintage capital growth models
In this papel, we examine techniques for the analytical and numerical solution of statedependent differential-difference equations. Such equations occur in the continuous time modelling of vintage capital growth models, which form a particularly important class of models in modern economic growth theory. The theoretical treatment of non-statedependent differential-difference equations in economics has already been discussed by Benhabib and Rustichini (1991). In general, though, the state-dependence of a model prevents its analytical solution in all but the simplest of cases. We review a numerical method for solving state-dependent models, using sorne simple examples to illustrate our discussion. In addition, we analyse the Solow vintage capital growth model. We conclude by mentioning a crucial unresolved issue related to this topic
Fast recursive filters for simulating nonlinear dynamic systems
A fast and accurate computational scheme for simulating nonlinear dynamic
systems is presented. The scheme assumes that the system can be represented by
a combination of components of only two different types: first-order low-pass
filters and static nonlinearities. The parameters of these filters and
nonlinearities may depend on system variables, and the topology of the system
may be complex, including feedback. Several examples taken from neuroscience
are given: phototransduction, photopigment bleaching, and spike generation
according to the Hodgkin-Huxley equations. The scheme uses two slightly
different forms of autoregressive filters, with an implicit delay of zero for
feedforward control and an implicit delay of half a sample distance for
feedback control. On a fairly complex model of the macaque retinal horizontal
cell it computes, for a given level of accuracy, 1-2 orders of magnitude faster
than 4th-order Runge-Kutta. The computational scheme has minimal memory
requirements, and is also suited for computation on a stream processor, such as
a GPU (Graphical Processing Unit).Comment: 20 pages, 8 figures, 1 table. A comparison with 4th-order Runge-Kutta
integration shows that the new algorithm is 1-2 orders of magnitude faster.
The paper is in press now at Neural Computatio
Adaptive transient solution of nonuniform multiconductor transmission lines using wavelets
Abstract—This paper presents a highly adaptive algorithm for the transient simulation of nonuniform interconnects loaded with arbitrary nonlinear and dynamic terminations. The discretization of the governing equations is obtained through a weak formula-tion using biorthogonal wavelet bases as trial and test functions. It is shown how the multiresolution properties of wavelets lead to very sparse approximations of the voltages and currents in typical transient analyzes. A simple yet effective time–space adaptive al-gorithm capable of selecting the minimal number of unknowns at each time iteration is described. Numerical results show the high degree of adaptivity of the proposed scheme. Index Terms—Electromagnetic (EM) transient analysis, multi-conductor transmission lines (TLs), wavelet transforms. I
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