In this paper we present a new approach to modeling dynamic production systems with discrete flow. This method is based on the automatic knowledge domain, in order to build a mathematical model that accurately formalizes the behavior of the studied system. The approach adopted for this study is the parametric identification of nonlinear systems (Hammerstein-Wiener system and NARX model). The pr...

Abstract Hammerstein–Wiener models constitute a significant class of block-structured dynamic models, as they approximate process nonlinearities on the basis input–output data without requiring identification full nonlinear model. Optimization problems with embedded are nonconvex, and thus local optimization methods may obtain suboptimal solutions. In this work, we develop deterministic global ...

In the note the class of block-oriented dynamic nonlinear systems is considered, in particular, Hammerstein and Wiener systems are investigated. Several algorithms for nonlinear system identification are presented. The algorithms exploit various degrees of prior knowledge from parametric to nonparametric. Eventually, a semiparametric algorithm, which shares advantages of both approaches is anno...

Hammerstein-Wiener systems present a structure consisting of three serial cascade blocks. Two are static nonlinearities, which can be described with nonlinear functions. The third block represents linear dynamic component placed between the first two Some common model structures include rational-type transfer function, orthogonal rational functions (ORF), finite impulse response (FIR), autoregr...

Power amplifiers (PAs) are inherently nonlinear elements and digital predistortion is a highly cost-effective approach to linearize them. Although most existing architectures assume that the PA has a memoryless nonlinearity, memory effects of the PAs in many applications ,such as wideband code-division multiple access (WCDMA) or orthogonal frequency-division multiplexing (OFDM), can no longer b...

A two-stage identification algorithm is introduced for tracking the parameters in time-varying Hammerstein-Wiener systems. The Kalman filtering and parameter separation technique are employed proposed algorithm. convergence analysis of this provided. It shown that can guarantee boundedness estimation error. Four simulation examples, including a practical system application electric arc furnace,...

This article reviews techniques to identify the frequency domain properties of linear and nonlinear systems from measured physical input/output data. Practical spectral analysis procedures are discussed that are valid with random data to predict the frequency response functions in single-input/single-output (SI/SO) linear models and in multipleinput/single-output (MI/SO) linear models. Special ...