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