Using Decoupling Methods to Reduce Polynomial NARX Models

David T. Westwick, Gabriel Hollander, Kiana Karami, Johan Schoukens

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


The polynomial NARX model, where the output is a polynomial function of past inputs and outputs, is a commonly used equation error model for nonlinear systems. While it is linear in the variables, which simplifies its identification, it suffers from two major drawbacks: the number of parameters grows combinatorially with the degree of the nonlinearity, and it is a black box model, which makes it difficult to draw any insights from the identified model. Polynomial decoupling techniques are used to replace the multiple-input single-output polynomial with a decoupled polynomial structure comprising a transformation matrix followed by bank of SISO polynomials, whose outputs are then summed. This approach is demonstrated on two benchmark systems: The Bouc-Wen friction model and the data from the Silverbox model. In both cases, the decoupling results in a substantial reduction in the number of parameters, and allows some insight into the nature of the nonlinearities in the system.

Original languageEnglish (US)
Pages (from-to)796-801
Number of pages6
Issue number15
StatePublished - Jan 1 2018

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


Dive into the research topics of 'Using Decoupling Methods to Reduce Polynomial NARX Models'. Together they form a unique fingerprint.

Cite this