Abstract
This paper presents an adaptive fuzzy tuner for the optimization of non-linear, multi-variable problems. The gradient-descent method is used to adaptively tune the bases of the membership functions used in the fuzzy logic optimization. The performance of the optimization with adaptive tuning is tested in comparison with fuzzy optimization without adaptive tuning of membership functions. It is shown that the adaptive fuzzy optimization provides better performance by converging to the optimal value in lesser time and fewer iterations. A multi-variable, non-linear optimization problem is shown as an illustrative example to demonstrate improved performance. This adaptive tuning scheme has also been incorporated into the Generalized Intelligent Grinding Advisory System (GIGAS II) and results show that even for very complex problems such as manufacturing processes, improved performance can be obtained.