A Markov chain approach to identification of the Wiener, Hammerstein, and nonlinear ARX (NARX) systems is presented. The motivation of this approach comes from the fact that these classes of nonlinear systems are connected with Markov chains, and hence their asymptotical properties, such as ergodicity, stationarity, and invariant probability distribution, can be derived from the corresponding c...

Control of a bio-reactor is a complex task due to inherent non-linearities and unavailability of measurements of the quality variables at regular sampling intervals. In this work, it proposed to identify Wiener-Hammerstein type fast-rate time series models for the quality variables directly from the irregularly sampled multi-rate input-output data. The identified models are further used to deve...

This paper describes a Matlab (or Octave) based software package for the estimation of dynamic systems. It has been developed primarily as a vehicle for profiling novel approaches relative to existing methods within a common software framework in order to streamline comparisons. Key features of the toolbox include simplicity of use (particularly via automated entry of unspecified values), the s...

Block-based nonlinear structures as, e.g., the Wiener-Hammerstein model, are popular for analyzing a broad class of nonlinear distortions. On the other hand, these models can also be related to Volterra filters in diagonal coordinates which represent very general nonlinear filters with memory. In this work, we propose an approach for estimating the significant coefficients of the nonlinear Volt...

In the paper we recover the static characteristic of Wiener-Hammerstein (sandwich) system from inputoutput data. The system is excited and disturbed by random processes with arbitrary distribution. Two kernel-based estimates are proposed and compared. It is shown that they can successfully recover the system characteristic under small amount of a priori information about the static characterist...

Abstract We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on kernels typically resort to coupled tensor decompositions However, in case systems, such methods require nontrivial constraints factors decompositions. In this paper, we propose an entirely different approach: by using special sampling (operating) points for Jacobian n...