WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification

  title={WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification},
  author={J. Zambrano and J. Sanchis and J. M. Dur{\'a} and Miguel A. Mart{\'i}nez},
Current methods to identify Wiener-Hammerstein systems using Best Linear Approximation (BLA) involve at least two steps. First, BLA is divided into obtaining front and back linear dynamics of the Wiener-Hammerstein model. Second, a refitting procedure of all parameters is carried out to reduce modelling errors. In this paper, a novel approach to identify Wiener-Hammerstein systems in a single step is proposed. This approach is based on a customized evolutionary algorithm (WH-EA) able to look… Expand
A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener-Hammerstein Models by Using WH-EA and Multistep Signals
Results show that the proposed approach is useful for identifying Wiener, Hammerstein, and Wiener–Hammerstein models, without requiring prior information on the type of structure to be identified. Expand
Wiener–Hammerstein System Identification: A Fast Approach Through Spearman Correlation
This paper proposes the use of the Spearman correlation to select good models for optimizing the Wiener–Hammerstein system and achieves a massive speedup in processing time without any prior knowledge about the system. Expand
WH-MOEA: A Multi-Objective Evolutionary Algorithm for Wiener-Hammerstein System Identification. A Novel Approach for Trade-Off Analysis Between Complexity and Accuracy
The results show that it can be useful to consider the simultaneously precision and complexity of a block-oriented model (Wiener, Hammerstein or Wiener-Hammerstein) in a non-linear process identification, with a trade-off between accuracy and model complexity. Expand
Instrumental Variable-Based OMP Identification Algorithm for Hammerstein Systems
A sparsity-seeking orthogonal matching pursuit (OMP) optimization method of compressive sensing is extended to identify parameters and orders of the Hammerstein system and results illustrate that the investigated method is effective and has advantages of simplicity and efficiency. Expand
Modelling of Uncertain System: A comparison study of Linear and Non-Linear Approaches
  • S. I. Abba, M. S. Gaya, +7 authors N. Wahab
  • Mathematics
  • 2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)
  • 2019
Modelling of a river involves protracted engagement with uncertainty, thus making developing a reliable model becomes quite cumbersome and often impossible. This paper presents a comparison of aExpand


Wiener–Hammerstein system identification – an evolutionary approach
A hybrid culture identification method is developed that involves model structure adaptation using genetic recombination and model parameter learning using particle swarm optimisation to identify parametric Wiener–Hammerstein systems. Expand
Identification of a Wiener–Hammerstein system using an incremental nonlinear optimisation technique
A method for black-box identification of a Wiener–Hammerstein system is described and applied to a set of Benchmark data originally presented at the 15th IFAC Symposium on System Identification. AnExpand
Identification of Wiener-Hammerstein systems by a nonparametric separation of the best linear approximation
This paper presents a nonparametric approach to separate the front and back dynamics starting from the best linear approximation (BLA) and the method is validated on the Wiener-Hammerstein benchmark. Expand
Identification of Wiener–Hammerstein models: Two algorithms based on the best split of a linear model applied to the SYSID'09 benchmark problem
This paper describes the identification of Wiener–Hammerstein models and two recently suggested algorithms are applied to the SYSID'09 benchmark data. The most difficult step in the identificationExpand
Initial estimates of the linear subsystems of Wiener-Hammerstein models
A scanning technique is developed that can efficiently evaluate each of the proposed initializations of the Wiener-Hammerstein models using estimates of some carefully constructed nonlinear characteristics of the system, estimates which can be formed using linear system identification techniques after some data pre-processing. Expand
Generalised Hammerstein–Wiener system estimation and a benchmark application
This paper examines the use of a so-called “generalised Hammerstein–Wiener” model structure that is formed as the concatenation of an arbitrary number of Hammerstein systems to be effective and, via Monte-Carlo simulation, relatively robust against capture in local minima. Expand
Nonlinear predictive control for Hammerstein-Wiener systems
This paper discusses a nonlinear Model Predictive Control (MPC) algorithm for multiple-input multipleoutput dynamic systems represented by cascade Hammerstein–Wiener models. The block-orientedExpand
A fractional approach to identify Wiener-Hammerstein systems
This paper shows that it is possible to generate initial estimates in an alternative way with no more than two iterative optimizations needed and that large model orders can be handled. Expand
Maximum likelihood identification of Wiener–Hammerstein models
Abstract The Wiener–Hammerstein (WH) model in discrete form is a cascaded connection of three blocks including a linear dynamics block, a static nonlinearity block, and a (secondary) linear dynamicsExpand
Wiener-Hammerstein Benchmark
This paper describes a benchmark for nonlinear system identification. A Wiener-Hammerstein system is selected as test object. In such a structure there is no direct access to the static nonlinearityExpand