In practice, it is usually difficult to obtain the physical model of nonlinear, rotor-bearing systems due to uncertain nonlinearities. In order to solve this issue to conduct the analysis and design of nonlinear, rotor-bearing systems, in this study, a data driven NARX (Nonlinear Auto-Regressive with exogenous inputs) model is identified. Due to the lack of the random input signal which is requ...

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...

A system identification method for nonlinear systems with unknown structure by means of short input-output data is proposed. This method introduces more general model structure for nonlinear systems. Moreover, based on gray-box idea and its salient feature with expanding NARMAX (Nonlinear Autoregressive, Moving Average eXogenous) modeling, this method integrates different system information. Th...

Foreign exchange market is one of the most complex dynamic market with high volatility, non linear and irregularity. As the globalization spread to the world, exchange rates forecasting become more important and complicated. Many external factors influence its volatility. To forecast the exchange rates, those external variables can be used and usually chosen based on the correlation to the pred...

Many of the popular nonlinear time series models require a priori the choice of parametric functions which are assumed to be appropriate in specific applications. This approach is used mainly in financial applications, when sufficient knowledge is available about the nonlinear structure between the covariates and the response. One principal strategy to investigate a broader class on nonlinear t...

In this article, we develop a linear theory for optimal filtering of complex turbulent signals with model errors through linear autoregressive models. We will show that when the autoregressive model parameters are chosen such that they satisfy absolute stability and consistency conditions of at least order-2 of the classical multistep method for solving initial value problems, the filtered solu...

We investigate the stability, in terms of V -uniform ergodicity or transience, of cyclic threshold autoregressive time series models. These models cycle through one of a number of collections of subregions of the state space when the process is large. Our results can be applied in cases where the model has multiple cycles and/or affine thresholds. The bounds on the parameter space are sharper t...

Discrete-time signal processing (DSP) tools have been used to analyze numerous optical filter configurations in order to optimize their linear response. In this paper, we propose a DSP approach to design nonlinear optical devices by treating the desired nonlinear response in the weak perturbation limit as a discrete-time filter. Optimized discrete-time filters can be designed and then mapped on...

This paper is motivated by recent evidence that many univariate economic and …nancial time series have both nonlinear and long memory characteristics. Hence, this paper considers a general nonlinear, smooth transition regime autoregression which is embedded within a strongly dependent, long memory process. A time domainMLE with simultaneous estimation of the long memory, linear AR and nonlinear...