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10.1038/srep10492 http://scihub22266oqcxt.onion/10.1038/srep10492 C4650685!4650685 !26020412     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26020412 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Rep 2015 ; 5 (ä): 10492 Nephropedia Template TP {{tp|p=26020412 |t=2015. Data Clustering using Memristor Networks |pdf=|usr=}}{{26020412 }} gab.com Text Data Clustering using Memristor Networks JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26020412 #FOAvip Memristors have emerged as a promising candidate for critical applications such as non-volatile memory as well as non-Von Neumann computing architectures based on neuromorphic and machine learning systems. In this study, we demonstrate that memristors can be used to perform principal component analysis (PCA), an important technique for machine learning and data feature learning. The conductance changes of memristors in response to voltage pulses are studied and modeled with an internal state variable to trace the analog behavior of the device. Unsupervised, online learning is achieved in a memristor crossbar using Sanger's learning rule, a derivative of Hebb's rule, to obtain the principal components. The details of weights evolution during training is investigated over learning epochs as a function of training parameters. The effects of device non-uniformity on the PCA network performance are further analyzed. We show that the memristor-based PCA network is capable of linearly separating distinct classes from sensory data with high clarification success of 97.6% even in the presence of large device variations. Twit Text FOAVip Data Clustering using Memristor Networks ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26020412 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26020412 #FOAvip English Wikipedia <ref>{{Cite pmid|26020412 }}</ref> Data Clustering using Memristor Networks #MMPMID26020412 Choi S ; Sheridan P ; Lu WD Sci Rep 2015[May]; 5 (ä): 10492 PMID26020412 show ga Memristors have emerged as a promising candidate for critical applications such as non-volatile memory as well as non-Von Neumann computing architectures based on neuromorphic and machine learning systems. In this study, we demonstrate that memristors can be used to perform principal component analysis (PCA), an important technique for machine learning and data feature learning. The conductance changes of memristors in response to voltage pulses are studied and modeled with an internal state variable to trace the analog behavior of the device. Unsupervised, online learning is achieved in a memristor crossbar using Sanger's learning rule, a derivative of Hebb's rule, to obtain the principal components. The details of weights evolution during training is investigated over learning epochs as a function of training parameters. The effects of device non-uniformity on the PCA network performance are further analyzed. We show that the memristor-based PCA network is capable of linearly separating distinct classes from sensory data with high clarification success of 97.6% even in the presence of large device variations. äData Clustering using Memristor Networks (epmc).pdf DeepDyve Pubget Overpricing