Abstract
This paper presents an improved modification of tantalum oxide memristor model and its application in neural networks. The proposed model is based on the standard Hewlett Packard tantalum oxide model with three improvements – application of a modified Biolek window function, optimization of its performance using simplified current-voltage relationship and by replacements of step model’s components by continuous differentiable functions. The optimal values of the tuning model’s coefficients are derived by comparison with experimental data and parameter estimation algorithm. A PSpice library memristor model is created in accordance to its mathematical model. The considered memristor model is applied in a simple neural network for function fitting with memristor-based synapses. A comparison with several existing tantalum oxide memristor models is made and the main advantages of the proposed model are established– higher performance, improved tuning capability and operation for hard-switching mode.
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