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Black-Box system identification by means of Support Vector Regression and Imperialist Competitive Algorithm

Sałat R., Awtoniuk M., Korpysz K.

Przegląd Elektrotechniczny

2013

R. 89, nr 9

223-226

The paper proposes to use SVR network for system identification by means of the Black-Box method. Selection of the optimal network parameters as well as the selection of optimal set of regressors by the Imperialist Competitive Algorithm have been proposed. The accuracy of models built was compared to ARX model for a linear object and NARX model for a non-linear one. The results show that the use of SVR in Black- Box identification can be an useful and efficient alternative for models based on NARX and ARX structures.

W artykule zaproponowano użycie sieci SVR do identyfikacji systemów metodą Black-Box. Do doboru optymalnych parametrów sieci i wyboru optymalnego zestawu regresorów, zaproponowano użycie algorytmu ewolucyjnego Imperialist Competitive Algorithm. Dokładność zbudowanych modeli porównano z modelem typu ARX, dla obiektu liniowego, oraz NARX, dla nieliniowego. Otrzymane wyniki wskazują, że wykorzystanie SVR w identyfikacji Black-Box może być użyteczną i efektywną alternatywą dla modeli o strukturze ARX oraz NARX.

Incrementally Solving Nonlinear Regression Tasks Using IBHM Algorithm

Zawistowski P., Arabas J.

Journal of Telecommunications and Information Technology

2011

nr 4

65-72

This paper considers the black-box approximation problem where the goal is to create a regression model using only empirical data without incorporating knowledge about the character of nonlinearity of the approximated function. This paper reports on ongoing work on a nonlinear regression methodology called IBHM which builds a model being a combination of weighted nonlinear components. The construction process is iterative and is based on correlation analysis. Due to its iterative nature, the methodology does not require a priori assumptions about the final model structure which greatly simplifies its usage. Correlation based learning becomes ineffective when the dynamics of the approximated function is too high. In this paper we introduce weighted correlation coefficients into the learning process. These coefficients work as a kind of a local filter and help overcome the problem. Proof of concept experiments are discussed to show how the method solves approximation tasks. A brief discussion about complexity is also conducted.