Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation
Abstract
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.
Keywords
Cite
@article{arxiv.1103.1156,
title = {Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation},
author = {Farnood Merrikh-Bayat and Saeed Bagheri-Shouraki},
journal= {arXiv preprint arXiv:1103.1156},
year = {2011}
}
Comments
34 pages, 14 figures, Submitted to IEEE Transactions on Fuzzy Systems