Adaptively Adjusted Footprint of Uncertainty in Interval Type 2 Fuzzy Controller for Cancer Drug Delivery

This paper presents chemotherapy scheduling of cancer patients using type 1 and type 2 fuzzy logic controllers which are optimized by genetic algorithm. To handle the uncertainties of the model, we introduce a method to adjust the foot print of uncertainty (FOU) in interval type 2 (IT2) fuzzy systems based on the amount of uncertainty. Based on previous researches, type two fuzzy logic is more effective than type 1 in handling uncertainties in a model. According to this fact, proposed method tries to change the FOU of fuzzy sets adaptively based on the amount of uncertainty in counting tumor cells which always exist in real world. In addition, we have introduced two new indices to evaluate the results. Simulation results show that the proposed method can control the drug regimens better than IT2 and type 1 (IT1) fuzzy controllers.