Nonlinear autoregressive processes constitute a potentially important class of nonlinear signal models for a wide range of signal processing applications involving both natural and man-made phenomena. A state space characterization is used to develop algorithms for modeling and estimating signals as nonlinear autoregressive processes from noise-corrupted measurements. Special attention is given...

This paper presents a technique for modeling nonlinear distortion of multirate time-varying communication circuits. To properly consider the weakly nonlinear distortion effects in circuits with multiple large-signal excitations, we capture the quasiperiodic boundary condition of the system Volterra kernels using a multivariate formulation. We then extend the model order reduction work of [8][9]...

( ) ABSTRACT: Artificial neural networks ANN recently gained attention as a fast and flexible vehicle to microwave modeling and design. Fast neural models trained from measured simulated microwave data can be used during microwave design to provide instant answers to the task they have learned. We review two important aspects of neural-network-based microwave modeling, namely, model development...

We investigate dynamic versions of fuzzy logic systems (FLS’s) and, specifically, their non-Singleton generalizations (NSFLS’s), and derive a dynamic learning algorithm to train the system parameters. The history-sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. This is illustrated through an example in nonline...

The nonlinear and dynamic accommodating capability of time domain models makes them a useful representation of chaotic time series for analysis, modeling and prediction. This paper is devoted to the modeling and prediction of chaotic time series with hidden attractors using a nonlinear autoregressive model with exogenous inputs (NARX) based on a novel recurrent fuzzy functions (RFFs) approach. ...

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust t...

SLAM: cross-species gene finding and alignment with a generalized pair hidden Markov model. Bayesian approach to reconstructing genetic regulatory networks with hidden factors. to analyze expression data. Estimation of genetic networks and functional structures between genes by using Bayesian networks and non-parametric regression. network and nonparametric heteroscedastic regression for nonlin...

Converting a nonlinear problem to a linear one by means of the least square fit, a nonlinear error modeling method based on measuring data is presented. Combined with an example, some key items are pointed out during modeling. The simulation results on parallel machine tool show that the model based on the method is of high accuracy and the error modeling method is correct and reliable. No matt...

In this tutorial we present recently developed nonlinear methods of cardiovascular physics and show their potentials to clinically relevant problems in cardiology. The first part describes methods of cardiovascular physics, especially data analysis and modeling of noninvasively measured biosignals, with the aim to improve clinical diagnostics and to improve the understanding of cardiovascular r...

Modeling is a prerequisite for the most fundamental signal processing tasks of signal analysis, detection, classification, denoising, and compression. While linear models are widely used, and they allow elegant theoretical and algorithmic developments, their nonlinear alternatives offer advantages in terms of modeling power and improved performance, which often eclipse the extra cost due to inc...