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- W85887990 abstract "A new approach for identification of nonlinear dynamic systems is proposed. It is based on a combination of generalized orthonormal basis functions and local linear model trees (LOLIMOT). The main idea is to approximate an unknown function from data by the interpolation of many local linear models. The number of local linear models and their validity regions are determined by a tree construction algorithm that partitions the input space by axisorthogonal cuts. The parameters of the local linear models are estimated by minimizing a equation error criterion (ARX models). These local parametric models are replaced by linear parameterized output error models based on generalized orthonormal basis functions. Thus, stability of the nonlinear dynamic model can be proven and the low bias property of output error models can be exploited. Furthermore, a subset selection technique may be applied for choosing the complexity of each local linear model. Since different basis functions may be constructed for each local linear model, processes with operating condition dependent dynamic behavior can be modeled efficiently." @default.
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- W85887990 date "1997-07-01" @default.
- W85887990 modified "2023-10-14" @default.
- W85887990 title "Orthonormal Basis Functions for Nonlinear System Identification with Local Linear Model Trees (LOLIMOT)" @default.
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- W85887990 doi "https://doi.org/10.1016/s1474-6670(17)42917-x" @default.
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