  In this paper a novel neuro-fuzzy system is proposed where its learning is
based on the creation of fuzzy relations by using new implication method
without utilizing any exact mathematical techniques. Then, a simple memristor
crossbar-based analog circuit is designed to implement this neuro-fuzzy system
which offers very interesting properties. In addition to high connectivity
between neurons and being fault-tolerant, all synaptic weights in our proposed
method are always non-negative and there is no need to precisely adjust them.
Finally, this structure is hierarchically expandable and can compute operations
in real time since it is implemented through analog circuits. Simulation
results show the efficiency and applicability of our neuro-fuzzy computing
system. They also indicate that this system can be a good candidate to be used
for creating artificial brain.
