Identification of fuzzy models for a glass furnace process

In this paper a study is described for several approaches to the identification of models for the temperature within the melter portion of a glass furnace. The focus is on developing models from the gas input to the throat (melter outlet) temperature. Conventional linear techniques for system identification proved to be inadequate for this problem, but proved useful as baseline comparisons for further studies involving nonlinear techniques from intelligent control for model building. Various combinations of input and output variables in a variety of model structures using fuzzy and neuro-fuzzy system modeling approaches are developed, and comparisons are drawn. Approaches reported on here investigate nonlinear Takagi-Sugeno (TS) fuzzy model formulations, where a linear-in-the-parameter identification problem is formulated for various combinations of measured variables and system delays. A fuzzy-neuro formulation is then discussed for parameter selection in the TS model structure while simultaneously optimizing the membership functions associated with the inputs of the TS fuzzy system. Simulation results for data collected from an operating glass furnace process are presented