A fuzzy-based parametric fault diagnosis approach for multiple memristor circuits

The memristor was originally defined as one of the fundamental electrical elements which provided the vacant connection between charge and flux. So far, most of the research has concentrated on the unique features of the individual devices. And the overall behavior of the multiple memristive systems has not been fully studied. Especially, the lack of corresponding fault diagnosis method for complex memristor circuits makes all the existing applications based on the multiple memristor circuits unstable and shaky. In this paper, the composite properties of multiple memristor circuits are further investigated. Then a neotype parametric fault diagnosis approach for memristive networks is presented using the doublet generator, sensitivity method and fuzzy math method, which offers huge benefits in terms of accuracy and time consumption.

[1]  R. Priester,et al.  New measures of testability and test complexity for linear analog failure analysis , 1981 .

[2]  Leon O. Chua,et al.  Composite Behavior of Multiple Memristor Circuits , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[3]  Leon O. Chua,et al.  Memristor Bridge Synapse-Based Neural Network and Its Learning , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[4]  M. Worsman,et al.  Enhancing the fault diagnosis of linear analog circuit steady-state DC testing through the analysis of equivalent faults , 2003 .

[5]  Peng Wang,et al.  A new diagnosis approach for handling tolerance in analog and mixed-signal circuits by using fuzzy math , 2005, IEEE Trans. Circuits Syst. I Regul. Pap..

[6]  Leon O. Chua,et al.  Memristor Bridge Synapses , 2012, Proceedings of the IEEE.

[7]  Hyongsuk Kim,et al.  Highly Accurate Doublet Generator for memristor-Based Analog Memory , 2012, Int. J. Bifurc. Chaos.

[8]  Leon O. Chua,et al.  Neural Synaptic Weighting With a Pulse-Based Memristor Circuit , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.

[9]  Shukai Duan,et al.  A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control , 2014, TheScientificWorldJournal.

[10]  Georgios Ch. Sirakoulis,et al.  On the generalization of composite memristive network structures for computational analog/digital circuits and systems , 2014, Microelectron. J..

[11]  Jeyavijayan Rajendran,et al.  Leveraging Memristive Systems in the Construction of Digital Logic Circuits , 2012, Proceedings of the IEEE.

[12]  Luigi Fortuna,et al.  A chaotic circuit based on Hewlett-Packard memristor. , 2012, Chaos.

[13]  F. Lombardi,et al.  Design of a Hybrid Memory Cell Using Memristance and Ambipolarity , 2013, IEEE Transactions on Nanotechnology.

[14]  Sung-Mo Kang,et al.  Resistive Computing: Memristors-Enabled Signal Multiplication , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[15]  L. Chua.  Memristor-The missing circuit element , 1971 .

[16]  Hong Wang,et al.  Soft Fault Diagnosis for Analog Circuits Based on Slope Fault Feature and BP Neural Networks , 2007 .

[17]  Farzan Aminian,et al.  Analog fault diagnosis of actual circuits using neural networks , 2002, IEEE Trans. Instrum. Meas..

[18]  Massimiliano Di Ventra,et al.  Experimental demonstration of associative memory with memristive neural networks , 2009, Neural Networks.