Data Representation in All-Resistor Systems-Reference-Cited by-同舟云学术

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Data Representation in All-Resistor Systems

Published:2020-10-02 Issue: Volume: Page:330-338
ISSN:1860-949X
Container-title:Advances in Neural Computation, Machine Learning, and Cognitive Research IV
language:
Short-container-title:

Author:

Kotov Vladimir B.,Beskhlebnova Galina A.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-60577-3_39

Reference9 articles.

1. Adamatzky, A., Chua, L.: Memristor Networks. Springer, Cham (2014)

2. Vaidyanathan, S., Volos, C.: Advances in Memristors, Memristive Devices and Systems. Springer, Cham (2017)

3. Xia, Q., Yang, J.J., Wu, W., Li, X., Williams, R.S.: Self-aligned memristor cross-point arrays fabricated with one nanoimprint lithography step. Nano Lett. 10(8), 2909–2914 (2010)

4. Kotov, V.B., Palagushkin, A.N., Yudkin, F.A.: Metaphorical modeling of resistor elements. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds.) Advances in Neural Computation, Machine Learning, and Cognitive Research III. NEUROINFORMATICS 2019. Studies in Computational Intelligence, vol. 856, pp. 326–334. Springer, Cham (2020)

5. Kotov, V.B., Yudkin, F.A.: Modeling and characterization of resistor elements for neuromorphic systems. Opt. Mem. Neural Netw. (Inf. Opt.) 28(4), 271–282 (2019)

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1. Using a Resistor Array to Tackle Optimization Problems;Studies in Computational Intelligence;2022-10-19

2. Specifics of Crossbar Resistor Arrays;Studies in Computational Intelligence;2022-10-19

3. Generation of the Conductivity Matrix;Advances in Neural Computation, Machine Learning, and Cognitive Research V;2021-11-23

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