Abstract
The memristor is a nano-size electrical device that is widely investigated in memory, logic circuits, and neuromorphic systems. This paper puts forward a general window function for Hewlett-Packard (HP) TiO2 memristor model, which covers almost all typical window functions and resolves the boundary lock problem, the inflexible parameter problem, and the distorted pinched hysteresis loop problem. Several adjustable parameters are introduced in the proposed window function in order to make it more flexible and scalable. A comparison with some classic window functions is given. Meanwhile, the range of these parameters is deduced with the aim of finding suitable parameters to simulate memristor devices. This TiO2 memristor model based on the novel window function is further applied to the spiking neural network and tested with the MNIST database of handwritten digits. The result demonstrates that the memristor-based neural network has higher recognition accuracy and better classification effect, indicating the availability of the entire scheme.
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