Operator tuning in fuzzy production rules using neural networks

In production rules, the total matching degree of the condition part is calculated from the matching degree of each condition by aggregation operators. In ordinal production systems, simple logical AND and OR functions are used as aggregation operators because the matching degrees are crisp values. In the case of fuzzy production rules, there are several promising approaches for handling the uncertainties of matching degrees. Investigations have been conducted on the automatic tuning of membership functions using neural networks. If a complex relationship exists between the conditions, tuning methods are not able to adjust for errors by tuning the membership functions. If similar characteristics exist in the relationships in some rule blocks, it must be possible to efficiently tune the rule blocks by tuning the aggregation operators. From this standpoint, as one step, the authors study tuning the aggregation operators. They consider automatic operator tuning of parametric T-norms and T-conorms whose characteristics can be modified parametrically