Indirect method of exponential convergence estimation for neural network with discrete and distributed delays

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dc.contributor.authorMartsenyuk, Vasyl
dc.date.accessioned2022-08-05T20:31:10Z
dc.date.available2022-08-05T20:31:10Z
dc.date.issued2017-10-06
dc.description.abstractThe purpose of this research is to develop method of calculation of exponential decay rate for neural network model based on differential equations with discrete and distributed delays. The method results in quasipolynomial inequality allowing us to investigate qualitative behavior of model in dependence on parameters. In such way it was shown direct dependency in changes of exponential decay rate and minimal threshold of distributed time delay. An example of two-neuron network with four delays is given and numerical simulations are performed to illustrate the obtained results. It was shown numerically that distributed delays combined with discrete delays narrow the interval of parameters admitting exponential convergence.
dc.description.departmentMathematics
dc.formatText
dc.format.extent12 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationMartenyuk, V. (2017). Indirect method of exponential convergence estimation for neural network with discrete and distributed delays. <i>Electronic Journal of Differential Equations, 2017</i>(246), pp. 1-12.
dc.identifier.issn1072-6691
dc.identifier.urihttps://hdl.handle.net/10877/16038
dc.language.isoen
dc.publisherTexas State University, Department of Mathematics
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceElectronic Journal of Differential Equations, 2017, San Marcos, Texas: Texas State University and University of North Texas.
dc.subjectNeural network model
dc.subjectExponential decay rate
dc.subjectDiscrete delays
dc.subjectDistributed delays
dc.subjectDelay differential equations
dc.titleIndirect method of exponential convergence estimation for neural network with discrete and distributed delays
dc.typeArticle

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