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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 08943370 |
| DOI: | 10.1002/jnm.2328 |
