Memristor resembles an artificial synapse and is considered to be basic electronic element for realizing neuromorphic circuits. In this work, a systematic investigation was conducted on memristor-based resistance-programming circuits to write analog data into a memristor utilizing pulse width modulation techniques. The high-frequency sinusoidal signal was utilized to read the data in the form of its electronic resistance. An optimum circuit configuration demonstrated multilevel stable resistive states, which are analogous to the connection weights in the human synapse. In order to modulate these memristive weights for representing the learning activities in human brain synapse, it was identified that the pulse width modulation technique is superior as compared to spike-timing-dependent plasticity. Further, the above analysis was utilized in training the memristor to update its resistive weights in consonance with its learning, analogous to that in a neural network. Further, the memristive crossbar architecture was utilized to implement a real-time application in Econometrics, where an array of memristors were utilized to learn and update the purchase trends of an [Formula: see text] matrix of customers. The proposed circuits possess the advantages of high packing density, low power consumption and nonvolatility, and also pave the way for developing future neuromorphic circuits.
INCREASING THE ENERGY EFFICIENCY OF THE AC ELECTRIC LOKOMOTIVE TRACTION DRIVE
