Exponential Stabilization of Memristive Neural Networks With Time Delays
Abstract
In this paper, a general class of memristive neural networks with time delays is formulated and studied. Some sufficient conditions in terms of linear matrix inequalities are obtained, in order to achieve exponential stabilization. The result can be applied to the closed-loop control of memristive systems. In particular, several succinct criteria are given to ascertain the exponential stabilization of memristive cellular neural networks. In addition, a simplified and effective algorithm is considered for design of the optimal controller. These conditions are the improvement and extension of the existing results in the literature. Two numerical examples are given to illustrate the theoretical results via computer simulations.
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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 [67]
Cites
Experimental demonstration of associative memory with memristive neural networks
Pershin, Y. V., & Di Ventra, M. (2010) · Neural Networks · doi:10.1016/j.neunet.2010.05.001 · Cited by 1,041 · see citing works
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Circuit design and exponential stabilization of memristive neural networks
Wen, S., Huang, T., Zeng, Z., Chen, Y., & Li, P. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.10.011 · Cited by 178 · see citing works
Self-citationOn the validity of memristor modeling in the neural network literature
Pershin, Y. V., & Di Ventra, M. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.08.026 · Cited by 49 · see citing works
“ristive” neural networks (MNNs) published during the last decade [1–60] (note that this list may be incomplete, as”
BackgroundMethod
- Authors
- Wu, A., & Zeng, Z.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2012-12
- Volume
- 23
- Issue
- 12
- Pages
- 1919-1929
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2092587774
- Cited by
- 295
- References
- 1
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 5 · 2023: 20 · 2022: 14 · 2021: 25 · 2020: 20 · 2019: 28 · 2018: 32 · 2017: 44 · 2016: 19 · 2015: 28 · 2014: 38 · 2013: 13
- FWCI
- 33.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Neural Networks Stability and Synchronization
- Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- memristive neural networks, time delay, exponential stabilization, linear matrix inequalities, feedback control, optimal controller design, stability criteria
- MeSH terms
- Time Factors, Neural Networks, Computer, Nanotechnology
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