Using memristor crossbar structure to implement a novel adaptive real time fuzzy modeling algorithm

TL;DR

This paper introduces a novel adaptive fuzzy modeling algorithm utilizing memristor crossbar hardware, combining active learning and fuzzy processing to enhance real-time system modeling and pattern recognition.

Contribution

It presents a new fuzzy modeling algorithm compatible with memristor crossbar hardware, integrating active learning for improved adaptability and stability in real-time applications.

Findings

Effective in modeling and pattern recognition tasks

Compatible with memristor crossbar hardware

Demonstrated through computer simulations

Abstract

Although fuzzy techniques promise fast meanwhile accurate modeling and control abilities for complicated systems, different difficulties have been re-vealed in real situation implementations. Usually there is no escape of it-erative optimization based on crisp domain algorithms. Recently memristor structures appeared promising to implement neural network structures and fuzzy algorithms. In this paper a novel adaptive real-time fuzzy modeling algorithm is proposed which uses active learning method concept to mimic recent understandings of right brain processing techniques. The developed method is based on processing fuzzy numbers to provide the ability of being sensitive to each training data point to expand the knowledge tree leading to plasticity while used defuzzification technique guaranties enough stability. An outstanding characteristic of the proposed algorithm is its consistency to memristor crossbar hardware processing concepts. An analog implemen-tation of the proposed algorithm on memristor crossbars structure is also introduced in this paper. The effectiveness of the proposed algorithm in modeling and pattern recognition tasks is verified by means of computer simulations