Fuzzy techniques can be used for accurate and high-speed modeling as well as for the control of complex systems, but various challenging problems are usually encountered during their actual implementation. For example, the variable parameters need to be optimized iteratively during the training phase, where this process is inspired by crisp domain algorithms. However, in recent years, memristor-based structures have emerged as another promising method for implementing neural network structures and fuzzy algorithms. In this study, we propose a novel adaptive and real-time fuzzy modeling algorithm, which employs the active learning method concept to mimic the functionality of the brain's right hemisphere. The proposed method processes fuzzy numbers such that the system retains its sensitivity to individual training data points, which expands the knowledge tree to allow plasticity, as well as using a specific defuzzification technique that guarantees stability. Another advantage of our new processing engine is that the nature of processing within the fuzzy system is consistent with that observed in memristive devices. Thus, we demonstrate that the proposed fuzzy architecture can be implemented easily and efficiently using existing crossbar structures. We verified the effectiveness of the proposed algorithm in modeling and pattern recognition tasks based on computer simulations.
