Efficient Simulation of Time-Derivative Cellular Neural Networks

Polat, S. Nergis Tural; Yavuz, Oguzhan; Tavşanoglu, V.

doi:10.1109/tcsi.2012.2189063

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Cited by 4 publications

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“…During the past decades, most research work on CNNs focused on two main fields. One is on CNN theories, such as the stability analysis [3][4][5][6][7], the convergence analysis [8], the chaos [9,10] and the improved CNN models [11][12][13][14][15]; another is on CNN applications, such as the hardware implementations [16][17][18], the image processing [19][20][21], the pattern recognition [22,23] and the associative memories [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

One-dimensional pairwise CNN for the global alignment of two DNA sequences

et al. 2015

Neurocomputing

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Section: Introductionmentioning

confidence: 99%

One-dimensional pairwise CNN for the global alignment of two DNA sequences

et al. 2015

Neurocomputing

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

Polat

2018

Int J Numerical Modelling

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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.

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