The Non-Stationary Response of Nonlinear Control Systems Subjected to a Suddenly Applied Stationary Random Signal

Nonlinear Multigrid Applied to a One-Dimensional Stationary Semiconductor Model

The nonlinear multigrid method is applied to a transistor problem in one dimension. A weak spot in the linearization of the well-known ScharfetterGummel discretization scheme is reported. Further it is shown that both the residual transfer and the solution transfer from a ne to a coarse grid need special requirements due to the rapidly varying problem coe cients. Some modi cations are proposed ...

Decomposition Theorems Applied to Random and Stationary Radar Targets

Transient Behaviors of Linear and Nonlinear Systems under Stationary Non-Gaussian Random Excitation

Time evolution of the probability density is investigated for initially quiescent dynamical systems exposed suddenly to random excitations. A variety of dynamical systems is considered, including the usual linear oscillator, and nonlinear oscillators of hardening spring, of softening spring, and of the van del Pol type. The random excitations are assumed to be stationary stochastic processes, s...

Using Wavelets and Splines to Forecast Non-Stationary Time Series

 This paper deals with a short term forecasting non-stationary time series using wavelets and splines. Wavelets can decompose the series as the sum of two low and high frequency components. Aminghafari and Poggi (2007) proposed to predict high frequency component by wavelets and extrapolate low frequency component by local polynomial fitting. We propose to forecast non-stationary process u...

Signal Processing Using Modular Struc- Tured Neural Network for Non-stationary and Nonlinear Acoustic Systems

Paying attention to realistic systems in the actual engineering fields, we must very often treat their systems as stochastic systems with non-Gaussian, nonlinear and/or non-stationary properties. In this paper, a regression analysis method for such stochastic systems is proposed by introducing reasonably a modular structured neural network. The proposed modular structured neural network is cons...