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Existence andp-exponential stability of periodic solution for stochastic shunting inhibitory cellular neural networks with time-varying delays
2016
International Journal of Computational Intelligence Systems
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In this paper, we investigate a class of stochastic shunting inhibitory cellular neural networks with time-varying delays. ...
Applying integral inequality, some sufficient conditions on the existence and pexponential stability of periodic solutions for stochastic shunting inhibitory cellular neural networks with time-varying ...
delays, Armmaret al. 4 made a detailed discussion on the existence and uniqueness of pseudo almost periodic solutions of recurrent neural networks with time-varying coef-ficients and mixed delays, Abbas ...
doi:10.1080/18756891.2016.1237192
fatcat:6nyc4du6lrek5fw4ghh3xb3bzm
Almost periodic solutions of retarded SICNNs with functional response on piecewise constant argument
[article]
2015
arXiv
pre-print
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The existence and exponential stability of almost periodic solutions are investigated. An illustrative example is provided. ...
We consider a new model for shunting inhibitory cellular neural networks, retarded functional differential equations with piecewise constant argument. ...
The second author is supported by the 2219 scholarship programme of TÜBİTAK, the Scientific and Technological Research Council of Turkey. ...
arXiv:1509.01079v1
fatcat:7ph6lgrr4faodavsdkbowxlpre
2021 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 32
2021
IEEE Transactions on Neural Networks and Learning Systems
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-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
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. ...
., +, TNNLS July 2021 3046-3055 On μ-Pseudo Almost Periodic Solutions for Clifford-Valued Neutral Type Neural Networks With Delays in the Leakage Term. ...
doi:10.1109/tnnls.2021.3134132
fatcat:2e7comcq2fhrziselptjubwjme
High Speed Photonic Neuromorphic Computing Using Recurrent Optical Spectrum Slicing Neural Networks
[article]
2022
arXiv
pre-print
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In this work, we present a new concept for realizing photonic recurrent neural networks and reservoir computing architectures with the use of recurrent optical spectrum slicing. ...
This new method for implementing recurrent neural processing in the photonic domain, which we call Recurrent Optical Spectrum Slicing Neural Networks, is numerically evaluated on a demanding, industry-relevant ...
Computing Using Recurrent Optical Spectrum Slicing Neural Networks: Supplementary Material . ...
arXiv:2203.15807v1
fatcat:a3ufaxfcrjbclg5vi2gmpwjd2a
Further Exploration on Bifurcation for Fractional-Order Bidirectional Associative Memory (BAM) Neural Networks concerning Time Delay∗
2021
Complexity
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Through the detailed analysis on the distribution of the roots of the characteristic equation of the involved fractional-order delayed BAM neural network systems, we set up a new delay-independent condition ...
This work principally considers the stability issue and the emergence of Hopf bifurcation for a class of fractional-order BAM neural network models concerning time delays. ...
[5] investigated the existence and global exponential stability of pseudo almost periodic solution to delayed BAM neural networks involving leakage delays by virtue of fixed point theory and mathematical ...
doi:10.1155/2021/9096727
doaj:8e6996a530f042c9a4880ee4569514e4
fatcat:h4d6zn333faqhn3v2im4el7xea
Global exponential stability of Clifford-valued neural networks with time-varying delays and impulsive effects
2021
Advances in Difference Equations
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AbstractIn this study, we investigate the global exponential stability of Clifford-valued neural network (NN) models with impulsive effects and time-varying delays. ...
By taking impulsive effects into consideration, we firstly establish a Clifford-valued NN model with time-varying delays. ...
with regard to jurisdictional claims in published maps and institutional affiliations. ...
doi:10.1186/s13662-021-03367-z
fatcat:2a4ouenesbf4dknhhyozfhbx6q
Metastable spiking networks in the replica-mean-field limit
[article]
2022
arXiv
pre-print
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Technically, these stationary rates are determined as the solutions of a set of delayed differential equations under certain regularity conditions that any physical solutions shall satisfy. ...
However, metastable dynamics typically unfold in networks with mixed inhibition and excitation. ...
Acknowledgments We acknowledge the support by the Provost's Graduate Excellence Fellowships at College of Natural Science at University of Texas at Austin; grant from Center of Theoretical and Computational ...
arXiv:2105.01223v3
fatcat:dkkluxmzojaa5gb3gk4j4xuice
Echo state networks with filter neurons and a delay∑ readout
2010
Neural Networks
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Echo state networks (ESNs) are a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. ...
Second, a delay∑ readout is introduced, which adds trainable delays in the synaptic connections of output neurons and therefore vastly improves the memory capacity of echo state networks. ...
Introduction In theory recurrent neural networks (RNNs) can approximate arbitrary nonlinear dynamical system with arbitrary precision (universal approximation property [24] ) and are also able to (re) ...
doi:10.1016/j.neunet.2009.07.004
pmid:19625164
fatcat:van6ndo7jfbp7crdt35smk2yyy
2021 Index IEEE Transactions on Cybernetics Vol. 51
2021
IEEE Transactions on Cybernetics
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-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
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 Nov. 2021 5259-5268 Almost Periodicity in Impulsive Fractional-Order Reaction-Diffusion Neural Networks With Time-Varying Delays. ...
doi:10.1109/tcyb.2021.3139447
fatcat:myjx3olwvfcfpgnwvbuujwzyoi
Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review
2020
Micromachines
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An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the ...
Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/mi11111021
pmid:33233457
fatcat:b7oc7duoxjd7rgzafeggdfh7ka
A Survey of Sound Source Localization with Deep Learning Methods
[article]
2022
arXiv
pre-print
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We provide an exhaustive topography of the neural-based localization literature in this context, organized according to several aspects: the neural network architecture, the type of input features, the ...
Tables summarizing the literature survey are provided at the end of the paper for a quick search of methods with a given set of target characteristics. ...
Convolutional recurrent neural networks CRNNs are neural networks containing one or more convolutional layers and one or more recurrent layers. ...
arXiv:2109.03465v3
fatcat:tq5vmgikwrenlbqba4lrqo3pee
Recurrent Neural Networks
[chapter]
2013
Intelligent Systems Reference Library
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Others address real-time solutions of optimization problems and a unified method for designing optimization neural network models with global convergence. ...
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
The Controlling of Transmission of Chaotic Signals in Communication Systems Based on Dynamic Models
2019
International Workshop on Computer Modeling and Intelligent Systems
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The article is devoted to the calculation of the characteristics of dynamic chaos based on the traffic of the corporate computer network. ...
An algorithm for the transmission of chaotic information using dynamic chaos based on the model of chaotic masking and nonlinear mixing of the information signal is proposed. ...
The delays in the arrival of the replicas of the signal are in the range from one to ∼20 quasi-periods of chaotic oscillations. ...
dblp:conf/cmis/BelejA19
fatcat:wq63amnixvfbphw6sixbazrx5i
Reservoir computing approaches to recurrent neural network training
2009
Computer Science Review
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Echo State Networks and Liquid State Machines introduced a new paradigm in artificial recurrent neural network (RNN) training, where an RNN (the reservoir ) is generated randomly and only a readout is ...
the reader with a detailed "map" of it. ...
Acknowledgments This work is partially supported by Planet Intelligent Systems GmbH, a private company with an inspiring interest in fundamental research. ...
doi:10.1016/j.cosrev.2009.03.005
fatcat:5572on2nqfcubewvymnjlvok6m
Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
2019
Materials
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These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. ...
Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems ...
Recognition of dynamic sequences may involve the use of recurrent neural network architectures or the resolution of continuous time differential equations. ...
doi:10.3390/ma12172745
pmid:31461877
pmcid:PMC6747825
fatcat:bt6hgscpczd2ldc4xc6np7wsvu
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