Memristor Crossbar-Based Hardware Implementation of Type-2 Fuzzy Membership Function and On-Chip Learning

Utilizing fuzzy techniques, especially fuzzy type-2, is one of the most widely used methods in machine learning to model uncertainty. in addition to algorithm provision, the hardware implementation capability, and proper performance in real-time applications are other challenges. the use of hardware platforms that have biological similarities and are comparable to human neural systems in terms of implementation volume has always been considered. memristor is one of the emerging elements for the implementation of fuzzy logic based algorithms. in this element, by providing current and selecting the appropriate direction for the applied current, the resistance of the memristor (memristance) will increase or decrease. various implementations of type-1 fuzzy (t1f) systems exist, but no implementation of type-2 fuzzy (t2f) systems has been done based on memristors. in this paper, memristor-crossbar structures are used to implement type-2 fuzzy membership functions. in the proposed hardware, the membership functions can have any shape and resolution. our proposed implementation of type-2 fuzzy membership function has the potential to learn (on-chip learning capability regardless of the host system). besides, the proposed hardware is analog and can be used as a basis in the construction of evolutionary systems. furthermore, the proposed approach is applied to memristor emulator to demonstrate its correct operation.