Autors: Mladenov, V. M.
Title: A Modified Tantalum Oxide Memristor Model for Neural Networks with Memristor-Based Synapses
Keywords: Improved window function, Neural network, PSpice library mod

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. 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.

References

    Issue

    9th International Conference on Modern Circuits and Systems Technologies, pp. 1-4, 2020, Germany, Institute of Electrical and Electronics Engineers Inc., DOI 10.1109/MOCAST49295.2020.9200238

    Цитирания (Citation/s):
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    Вид: постер/презентация в международен форум, публикация в реферирано издание, индексирана в Scopus