Dynamic behaviors of memristor-based recurrent neural networks with time-varying delays
Abstract
The paper introduces a general class of memristor-based recurrent neural networks with time-varying delays. Conditions on the nondivergence and global attractivity are established by using local inhibition, respectively. Moreover, exponential convergence of the networks is studied by using local invariant sets. The analysis in the paper employs results from the theory of differential equations with discontinuous right-hand sides as introduced by Filippov. The obtained results extend some previous works on conventional recurrent neural networks.
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Physical memristive elements behave differently from type 1 devices.
“(definitely the physical memristive elements behave differently [66]).”
From On the validity of memristor modeling in the neural network literature (2020) · cites this paper as [66]
The threshold-type model [62, 67] mimics the most common bipolar memristive elements [66].
“Here, the memristive device is exemplified by a threshold-type model [62, 67] that mimics the most common bipolar memristive elements [66]”
From On the validity of memristor modeling in the neural network literature (2020) · cites this paper as [66]
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References 40 · Cited by 185
- Authors
- Wu, A., & Zeng, Z.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Zeng, Zhigang
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2012-12
- Volume
- 36
- Pages
- 1-10
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W1994504172
- Cited by
- 185
- References
- 40
- Citations by year
- 2025: 2 · 2024: 4 · 2022: 11 · 2021: 7 · 2020: 10 · 2019: 9 · 2018: 21 · 2017: 18 · 2016: 23 · 2015: 22 · 2014: 39 · 2013: 19
- FWCI
- 33.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Neural Networks Stability and Synchronization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- memristor-based neural networks, recurrent neural networks, time-varying delays, global attractivity, exponential convergence, local inhibition, discontinuous right-hand side
- MeSH terms
- Algorithms, Electricity, Time Factors, Computer Storage Devices, Neural Networks, Computer, Nonlinear Dynamics
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