Passivity of Memristive BAM Neural Networks with Probabilistic and Mixed Time-Varying Delays
Open accessAbstract
This paper is concerned with the passivity problem of memristive bidirectional associative memory neural networks (MBAMNNs) with probabilistic and mixed time-varying delays. By applying random variables with Bernoulli distribution, the information of probability time-varying delays is taken into account. Furthermore, we consider the probability distribution of the variation and the extent of the delays; therefore, the results derived are less conservative than in the existing papers. In particular, the leakage delays as well as distributed delays are all taken into consideration. Based on appropriate Lyapunov-Krasovskii functionals (LKFs) and some useful inequalities, several conditions for passive performance are established in linear matrix inequalities (LMIs). Finally, numerical examples are given to demonstrate the feasibility of the presented theories, and the results reveal that the probabilistic and mixed time-varying delays have an unstable influence on the system and should not be ignored.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [62]
Cited by
Sorted by citations
On 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
- Authors
- Wang, W., Wang, M., Luo, X., Li, L., & Zhao, W.
- Affiliations
- University of Science and Technology Beijing (CN)
- Beijing University of Posts and Telecommunications (CN)
- Cleveland State University (US)
- Corresponding author
- Wang, Weiping
- Luo, Xiong
- Venue
- Mathematical Problems in Engineering
- Publisher
- Wiley
- Published
- 2018
- Volume
- 2018
- Pages
- 1-25
- ISSN
- 1024-123X (ISSN-L)
- 1563-5147
- OpenAlex
- W2795231536
- Cited by
- 1
- References
- 0
- Citations by year
- 2019: 1
- FWCI
- 0.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Mathematical Problems in Engineering (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Key Research and Development Program of China (2017YFB0702300, 61603032, 61174103, 06500025, 2015BAK38B01, TW201705)
- China Scholarship Council (2017YFB0702300, 61603032, 61174103, 06500025, 2015BAK38B01, TW201705)
- National Natural Science Foundation of China (2017YFB0702300, 61603032, 61174103, 06500025, 2015BAK38B01, TW201705)
- Fundamental Research Funds for the Central Universities (2017YFB0702300, 61603032, 61174103, 06500025, 2015BAK38B01, TW201705)
- National Key Technologies R&D Program of China (2017YFB0702300, 61603032, 61174103, 06500025, 2015BAK38B01, TW201705)
- University of Science and Technology Beijing-National Taipei University of Technology Joint Research Program (2017YFB0702300, 61603032, 61174103, 06500025, 2015BAK38B01, TW201705)
- 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
- passivity, memristive neural networks, bidirectional associative memory neural networks, mixed time-varying delays, Bernoulli distribution, leakage delay, distributed delay, Lyapunov-Krasovskii functional, linear matrix inequalities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
