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The vector‐matrix form numerical simulations for time‐derivative cellular neural networks.

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Title: The vector‐matrix form numerical simulations for time‐derivative cellular neural networks.
Authors: Tural Polat, Sadiye Nergis1 nergis@yildiz.edu.tr
Source: International Journal of Numerical Modelling. Sep/Oct2018, Vol. 31 Issue 5, p1-1. 13p.
Subjects: Ordinary differential equations, Cellular neural networks (Computer science), Discrete-time systems, MatLab (Computer software), Simulation methods & models
Abstract: Abstract: Time‐derivative cellular neural network (TDCNN) state equations can be written in vector‐matrix form which enables the application of discrete‐time numerical simulation methods. In this paper, existing numerical simulation methods are adapted for TDCNN for the first time, namely, MATLAB ordinary differential equation simulation and the vector‐matrix fourth‐order Runge‐Kutta approximation. Afterwards, several simulation methods for TDCNN are analyzed. The ordinary differential equation solvers in MATLAB program, fourth‐order Runge‐Kutta approximation, and the forward Euler approximation are used in the numerical simulation of the vector‐matrix form TDCNN. Our previously proposed fast simulation method for TDCNNs is revisited. The methods are discussed from a programmer's point of view, and the results are presented. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Numerical Modelling is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The vector‐matrix form numerical simulations for time‐derivative cellular neural networks.
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  Data: <searchLink fieldCode="DE" term="%22Ordinary+differential+equations%22">Ordinary differential equations</searchLink><br /><searchLink fieldCode="DE" term="%22Cellular+neural+networks+%28Computer+science%29%22">Cellular neural networks (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete-time+systems%22">Discrete-time systems</searchLink><br /><searchLink fieldCode="DE" term="%22MatLab+%28Computer+software%29%22">MatLab (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink>
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  Data: Abstract: Time‐derivative cellular neural network (TDCNN) state equations can be written in vector‐matrix form which enables the application of discrete‐time numerical simulation methods. In this paper, existing numerical simulation methods are adapted for TDCNN for the first time, namely, MATLAB ordinary differential equation simulation and the vector‐matrix fourth‐order Runge‐Kutta approximation. Afterwards, several simulation methods for TDCNN are analyzed. The ordinary differential equation solvers in MATLAB program, fourth‐order Runge‐Kutta approximation, and the forward Euler approximation are used in the numerical simulation of the vector‐matrix form TDCNN. Our previously proposed fast simulation method for TDCNNs is revisited. The methods are discussed from a programmer's point of view, and the results are presented. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Numerical Modelling is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/jnm.2328
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      – Code: eng
        Text: English
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        PageCount: 13
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      – SubjectFull: Ordinary differential equations
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      – SubjectFull: Cellular neural networks (Computer science)
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      – SubjectFull: Discrete-time systems
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      – SubjectFull: MatLab (Computer software)
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      – SubjectFull: Simulation methods & models
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      – TitleFull: The vector‐matrix form numerical simulations for time‐derivative cellular neural networks.
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              Text: Sep/Oct2018
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