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Mean-Square Exponential Input-to-State Stability of Stochastic Fuzzy Recurrent Neural Networks with Multiproportional Delays and Distributed Delays
2018
Mathematical Problems in Engineering
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We are interested in a class of stochastic fuzzy recurrent neural networks with multiproportional delays and distributed delays. ...
input-to-state stability of the suggested system. ...
Acknowledgments This work was jointly supported by the National Natural Science Foundation of China (61773217 and 61374080), the Natural Science Foundation of Jiangsu Province (BK20161552), and Qing Lan ...
doi:10.1155/2018/6289019
fatcat:nr6cy4mar5hhdfvszuvjwqi7si
Mean almost periodicity and moment exponential stability of semi-discrete random cellular neural networks with fuzzy operations
2019
PLoS ONE
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By using the semi-discretization technique of differential equations, the discrete analogue of a kind of cellular neural networks with stochastic perturbations and fuzzy operations is formulated, which ...
Finally, a problem of stochastic stabilization for discrete cellular neural networks is studied. ...
Acknowledgments The authors would like to extend their thanks to the referees for their careful reading of the manuscript and insightful comments. ...
doi:10.1371/journal.pone.0220861
pmid:31390372
pmcid:PMC6685627
fatcat:6zodok445vezffglpoene4jna4
Mean-square exponential stability of fuzzy stochastic BAM networks with hybrid delays
2018
Advances in Difference Equations
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We study fuzzy stochastic bidirectional associative memory cellular neural networks with discrete delays in leakage terms and with continuous and infinitely distributed delays in the transmission terms ...
Under certain structural assumptions, we prove that the networks in question are mean-square exponentially stable. ...
Acknowledgements Fosheng is supported by the Initial Foundation of Mianyang Teachers' College (Grant No. QD2016A003). ...
doi:10.1186/s13662-018-1690-z
fatcat:t3ce6qb3wvgqzg6smscopr2d2a
Stochastic Synchronization of Reaction-Diffusion Neural Networks under General Impulsive Controller with Mixed Delays
2012
Abstract and Applied Analysis
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This paper investigates drive-response synchronization of a class of reaction-diffusion neural networks with time-varying discrete and distributed delays via general impulsive control method. ...
Based on a novel impulsive differential inequality, the properties of random variables and Lyapunov functional method, sufficient conditions guaranteeing the global exponential synchronization in mean ...
The authors of 25 studied exponential stability of reaction-diffusion Cohen-Grossberg neural networks with time-varying discrete delays and stochastic perturbations. ...
doi:10.1155/2012/603535
fatcat:tjsorvjvdrbofgsjksefth56r4
2021 Index IEEE Transactions on Cybernetics Vol. 51
2021
IEEE Transactions on Cybernetics
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Note that the item title is found only under the primary entry in the Author Index. ...
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
., +, TCYB May 2021 2457-2465 Multiple Mittag-Leffler Stability of Delayed Fractional-Order Cohen-Grossberg Neural Networks via Mixed Monotone Operator Pair. ...
doi:10.1109/tcyb.2021.3139447
fatcat:myjx3olwvfcfpgnwvbuujwzyoi
Stability of stochastic neural networks of neutral type with Markovian jumping parameters: A delay-fractioning approach
2014
Journal of the Franklin Institute
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This paper deals with the stochastically asymptotic stability in the mean square for a new class of stochastic neural networks of neutral type with both Markovian jump parameters and mixed time delays. ...
jumping stochastic Cohen-Grossberg neural networks with discrete interval and distributed delays; Very recently, Zhu and Cao studied the exponential stability for several new classes of Markovian jump ...
Moreover, we have investigated stochastically asymptotic stability in the mean square of Markovian jump stochastic neutral type neural networks and the result has been derived by constructing a new Lyapunov-Krasovskii ...
doi:10.1016/j.jfranklin.2013.11.017
fatcat:k4cmwqisdnfz3fwx2wwnqggnue
A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case
2003
Neural Computation
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Spatiotemporal connectionist networks (STCNs) comprise an important class of neural models that can deal with patterns distributed in both time and space. ...
In this article, we widen the application domain of the taxonomy for supervised STCNs recently proposed by Kremer (2001) to the unsupervised case. ...
We also thank the reviewers for their insightful suggestions to improve the article. ...
doi:10.1162/089976603321780281
pmid:12816574
fatcat:duvonguvxrbnxbicygfuypumhm
2021 Index IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 51
2021
IEEE Transactions on Systems, Man & Cybernetics. Systems
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Note that the item title is found only under the primary entry in the Author Index. ...
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
., TSMC July 2021 4227-4237 Multiple ψ-Type Stability of Cohen-Grossberg Neural Networks With Unbounded Time-Varying Delays. ...
doi:10.1109/tsmc.2021.3136054
fatcat:b5hcsfwjw5hllpenqmaq6wpke4
A Delay-Dividing Approach to Robust Stability of Uncertain Stochastic Complex-Valued Hopfield Delayed Neural Networks
2020
Symmetry
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In the present study, a delay-dividing approach is devised to study the robust stability issue of uncertain neural networks. ...
Specifically, the uncertain stochastic complex-valued Hopfield neural network (USCVHNN) with time delay is investigated. Here, the uncertainties of the system parameters are norm-bounded. ...
Acknowledgments: The authors are grateful to the support provided by Chiang Mai University.
Conflicts of Interest: The authors declare no conflict of interest. Symmetry 2020, 12, 683 ...
doi:10.3390/sym12050683
fatcat:iax6p2zzkbawdbjis2duny5f7m
2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31
2020
IEEE Transactions on Neural Networks and Learning Systems
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., +, TNNLS Jan. 2020 331-344 Multistability of Almost Periodic Solution for Memristive Cohen-Grossberg Neural Networks With Mixed Delays. ...
doi:10.1109/tnnls.2020.3045307
fatcat:34qoykdtarewhdscxqj5jvovqy
Robustness Analysis of Exponential Stability of Neutral-type Nonlinear Systems with Multi-interference
2021
IEEE Access
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With a view to the unfavorable impact of the inevitable exogenous interferences for the practical engineering and signal transmission, here we focus on the robustness of global exponential stability for ...
Through the strategies mentioned above, a class of algebraic problems of estimating three upper bounds by solving transcendental equations with three variables is settled. ...
[33] studied the interval fuzzy robust exponential stability of Cohen-Grossberg networks by means of the comparison principle. Ref. ...
doi:10.1109/access.2021.3105521
fatcat:q3g5w7427baxlpdwyjuf25suwu
Recurrent Neural Networks: Associative Memory and Optimization
2011
Journal of Information Technology & Software Engineering
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A learning problem in a Hopfield network with J units can be transformed into a learning problem for a perceptron of dimension ( 1) 2 J J + [99], and thus every learning ...
Due to feedback connections, recurrent neural networks (RNNs) are dynamic models. ...
Equation (9) is a special case of the Cohen-Grossberg model [24] . An inspection of (6) and (3) shows that α is zero in the basic Hopfield model. ...
doi:10.4172/2165-7866.1000104
fatcat:zqfpc2fpwzhufigwozssmylotu
Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach
2011
Expert systems with applications
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The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. ...
Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. ...
is known as the stability-plasticity dilemma (Grossberg, 1982) . ...
doi:10.1016/j.eswa.2010.07.116
pmid:32288336
pmcid:PMC7126939
fatcat:lnjtwuli3bh5nbqvvxzleu4oki
Recurrent Neural Networks
[chapter]
2013
Intelligent Systems Reference Library
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Some authors discuss aspects of improving recurrent neural network performance and connections with Bayesian analysis and knowledge representation, including extended neuro-fuzzy systems. ...
Problems dealing with trajectories, control systems, robotics, and language learning are included, along with an interesting use of recurrent neural networks in chaotic systems. ...
Lund and Stefano Nolfi who did most of the development of the kepsim simulator, which has been used (in slightly adapted form) to implement the experiments documented in this paper. ...
doi:10.1007/978-3-642-36657-4_2
fatcat:jnmgv7rlifhuncqhi5kxldepdm
Artificial neural networks
1988
IEEE Circuits & Devices
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The mean-square error criterion is defined as: , (2.124) where E(.) is the expectation operator, and e=t-y is the error vector. ...
Class.
3
70
0.832
4
17
0.838
τ
10
6
-
=
Table 3 .1 -Parameters 3 Mean
Covariance Matrices
Table 4 . 1 - 41 Correspondence between Cohen-Grossberg and Hopfield modelsCohen-Grossberg ...
If we can find a Lyapunov function for the particular system at hand we prove global stability of the system. ...
doi:10.1109/101.8118
fatcat:c6royelcdzfethnmmur2tj524i
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