Abstract
The Fuzzy Multiple Regression Model (FMR Model) is a powerful non-linear identification tool, which has been applied to actual process controls and demonstrated its effectiveness. A major problem, however, is hardness to determine its rules and membership functions properly.In this paper, we discuss how to optimize the FMR Model automatically with data obtained from an actual plant. First optimization with considering the interaction between membership functions and rules is shown to be essential to obtain the accurate FMR Model. Then the optimization algorithm based on the simulated annealing is proposed, which is performed interactively with the multiple regression analysis for deciding consequent part of rules. How to accelerate the algorithm is also investigated from a viewpoint of the decrease of the number of multiple regression analysis executions. Finally usefulness of the developed method is evaluated with several simulation results, where it is applied to create a coating weight prediction model used in a continuous galvanizing line.
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