A kernel-based approach for nonlinear modeling of time series data is proposed in this paper. Autoregressive modeling is achieved in a feature space defined by a kernel function using a linear algorithm. The method extends the advantages of the conventional autoregressive models to characterization of nonlinear signals through the intelligent use of kernel functions.Experiments with synthetic s...

The auxiliary differential equation finite-difference time-domain method for modeling electromagnetic wave propagation in dispersive nonlinear materials is applied to problems where the electric field is not constrained to a single vector component. A full-vector Maxwell's equations solution incorporating multiple-pole linear Lorentz, nonlinear Kerr, and nonlinear Raman polarizations is present...

Nonlinear modeling routines are often applied in an effort to extract underlying determinism from time series data. The best of these methods perform well for short noisy time series when there is determinism in the underlying system. We show that nonlinear modeling does not distinguish between a static nonlinear transformation of linearly filtered noise and dynamic nonlinearity. To relieve thi...

Structural models are tools conceptually useful in time series modeling, because they allow the individual interpretation behavior of each structural component in the series; however, the difficult in the representation of nonlinear relationships of these models, have despised their use in real series. The Artificial Neural Networks (RNA) models are a promising alternative by the nonlinear time...

In this paper, a new nonlinear wavelet identification structure is proposed for high noise resistive soft sensors. This method uses proposedPolynomial Nonlinear Auto Regressive Exogenous Model, which can be solved with linear Gaussian Least Square Method, alongside the Averaging Wavelet Method (AWM) filter. AWM uses the approximation spaces for analyzing the signals and reduce the noise by a me...

Numerous natural and human-made systems can be described as nonlinear or complex. Such systems often escape the straight cause-effect and linear modeling patterns which traditional science has successfully used over centuries. Linear approximations of dynamic phenomena rarely deliver satisfactory results. On the other hand, nonlinear modeling techniques became feasible and more popular only wit...

Knitted fabrics are widely used by the underwear apparel industry due to their good elasticity. Modeling the mechanical behavior of knitted fabrics using Kelvin model is one of the discussed subjects in the textile industry. The purpose of this study is to investigate the accuracy of a nonlinear Kelvin model for determining the drying behavior of knitted fabrics. To fulfill this aim, genetic al...

We analyze a set of complex time series from the view point of nonlinear causality. The mathematical background for analyzing time series is an extension of embedding theories of autonomous systems to an input{output system. We consider that the existence of nonlinear causality can be detected by nonlinear predictability of input and output sequences. Several numerical examples are given for co...