Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Fuzzy Real Time Scheduling

The classical analysis of real time systems tries to ensure that the instance of every task finishes before its absolute deadline (strict guarantee). The probabilistic approach tends to estimate the probability that it will happen. The deterministic timed behavior is an important parameter for analysing the robustness of the system. Most of related works are mainly based on the determinism of time constraints. However, in most cases, these parameters are non-precise. The vagueness of parameters suggests the use of fuzzy logic to decide in what order the requests should be executed to reduce the chance of a request being missed. There are two common inference methods Mamdani’s fuzzy inference method and TakagiSugeno-Kang, method of fuzzy inference. The results of the two fuzzy inference systems (FIS) for generated output are compared. This paper outlines the basic difference between the Mamdani-type FIS and Sugeno-type FIS. It also shows which one is a better choice of the two FIS for real time system.