Automatic model selection in local linear model trees (LOLIMOT) for nonlinear system identification of a transport delay process

In this paper a new method for nonlinear system identification is proposed. It is based on local linear models constructed by a tree algorithm in combination with a subset selection technique for determination of the local linear models' structure. The local linear model tree can be interpreted as a Takagi-Sugeno fuzzy model, where the tree algorithm constructs the rule premises, the input membership functions and allows easy control of the model's complexity (number of rules) while the subset selection technique determines the rule conclusions. The selection of the local linear model structure allows an automatic choice of different model orders and dead times in different operating regions. The capability of this approach to model a real world process with operating point dependent dead times and time constants is demonstrated.