Abstract
This paper presents, a design method for the template of a hole-filler used to improve the pe rformance of the handwritten character recognition using numerical integration algorithms, based on the dynamic analysis of a cellular neural network (CNN). This is done by analyzing the features of the hole-filler template and the dynamic process of CNN using popular numerical integration algorithms to obtain a set of inequalities satisfying its output characteristics as well as the parameter range of the hole-filler template. Simulation results are presented for Euler, Modified Euler and RK methods and compared. It was found that RK Method performs well in terms of settling time and computation time for all step sizes.
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Murugesan, K., Elango, P. (2007). CNN Based Hole Filler Template Design Using Numerical Integration Techniques. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_50
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DOI: https://doi.org/10.1007/978-3-540-74690-4_50
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