Abstract

The applications of cyber-physical systems (CPS), which have a wide range from industrial to medical, are increasing day by day thanks to its reliable, scalable and flexible structure. In a CPS, the consistency and reliability of system are much more important, because they are generally used in large-scale and critical tasks. Uncertainties are unexpected situations and no matter how well a system designed they are a threat to a system always. Fuzzy logic is one of the algorithms that can be utilized in cyber layer easily. But because of its insufficiency in handling uncertainties new fuzzy types are emerged. Nonstationary fuzzy system is a type of fuzzy logic which is able to handle uncertainty in reasonable time. In this study a new inference system for nonstationary fuzzy systems is developed to enhance nonstationary fuzzy systems. The system is based on two main steps, first adding some random uncertainties to nonstationary inputs, and second obtaining single output value for the inputs. Thus, the fuzzy system always has uncertainty and the behavior of system is prepared for the uncertainties. The proposed method is verified by simulation results which demonstrate the effectiveness of system especially for noisy data compared to the type-1, and nonstationary fuzzy systems. The proposed method can be used in CPS which need consistency and robustness.

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