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Memristive neuro-fuzzy system

Merrikh Bayat, F ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1109/TSMCB.2012.2205676
  3. Publisher: 2013
  4. Abstract:
  5. In this paper, a novel neuro-fuzzy computing system is proposed where its learning is based on the creation of fuzzy relations by using a new implication method without utilizing any exact mathematical techniques. Then, a simple memristor cross-bar-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 adjust them precisely. Finally, this structure is hierarchically expandable, and it can do fuzzy operations in real time since it is implemented through analog circuits. Simulation results confirm 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
  6. Keywords:
  7. Fuzzy inference ; Memristor crossbar ; Neuro-fuzzy computing system ; Artificial brain ; Fuzzy operation ; Fuzzy relations ; Hardware implementations ; High connectivity ; Memristor ; Neuro-fuzzy computing ; Neurofuzzy system ; Fault tolerant computer systems ; Fuzzy inference ; Fuzzy systems ; Hardware ; Memristors ; Passive filters ; Analog circuits
  8. Source: IEEE Transactions on Cybernetics ; Volume 43, Issue 1 , January , 2013 , Pages 269-285 ; 21682267 (ISSN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6248730