In this article the numerical approximation of solutions of Itô stochastic differential equations is considered, in particular for equations with a small parameter 2 in the noise coefficient. We construct stochastic linear multi-step methods and develop the fundamental numerical analysis concerning their mean-square consistency, numerical stability in the mean-square sense and mean-square conve...

In radar signature analysis, the root mean square (RMS) surface slope is utilized to assess the relative contribution of multiple scattering effects. For an exponentially correlated surface, an effective RMS slope can be determined by truncating the high frequency tail of the roughness spectrum. The choice of the cutoff frequency and the effect on surface scattering simulations are discussed.

We investigate the long term behavior of solutions to a stochastic Volterra integro-differential equation with a fading memory; the fading memory is represented by using a decaying exponential convolution kernel. We give sufficient conditions for asymptotic mean square stability of the solution. In a similar spirit, we investigate the long term behavior of solutions to discrete analogues of the...

The thermal mean-square displacement (MSD) of hydrogen in proteins and its associated hydration water is measured by neutron scattering experiments and used an indicator of protein function. The observed MSD as currently determined depends on the energy resolution width of the neutron scattering instrument employed. We propose a method for obtaining the intrinsic MSD of H in the proteins, one t...

The problem of the stability of a class of stochastic linear hybrid systems with a special structure of matrices and a multiplicative excitation is considered. Sufficient conditions of the exponential p-th mean stability and the almost sure stability for a class of stochastic linear hybrid systems with the Markovian switching are derived. Also, sufficient conditions of the exponential mean-squa...

In this paper, the global exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs) with time delays and Markovian jumping parameters. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. The purpose of the prob...

The paper presents a new method for all-pole model estimation based on minimization of the weighted mean square error in the sampled spectral domain. Due to discrete nature of the proposed distance measure, emphasis can be put on an arbitrary set of spectral samples what can greatly improve the model accuracy for periodic signals. Weighting can also be applied to improve the fitting in certain ...

Neural networks for estimation, such as the multilayer perceptron (MLP) and functional link net (FLN), are shown to approximate the minimum mean square estimator rather than the maximum likelihood estimator or others. Cramer-Rao maximum a posteriori lower bounds on estimation error can therefore be used to approximately bound network training error, when a statistical signal model is available ...

Best mean square prediction for moving average time series models is generally non-linear prediction, even in the invertible case. Gaussian processes are an exception, since best linear prediction is always best mean square prediction. Stable numerical recursions are proposed for computation of residuals and evaluation of unnormalized conditional distributions in invertible or non-invertible mo...

In 1991, J. E. Thomson determined completely the structure of H2(μ), the closed subspace of L2(μ) that is spanned by the polynomials, whenever μ is a compactly supported measure in the complex plane. As a consequence he was able to show that if H2(μ) = L2(μ), then every function f ∈ H2(μ) admits an analytic extension to a fixed open set Ω, thereby confirming in this context a phenomenon noted e...