Abstract
There are different approximations to improve the performance and mathematical representation of a cellular neural networks to work with linearly nonseparable data as XOR. But the main goal is to work with problems that only can solved with universal machines such as the game of life. In this paper a new model of Polynomial Cellular Neural Networks that solves the game of life is presented with the learning design to compute the templates.
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Gomez-Ramirez, E., Pazienza, G.E. (2007). The Game of Life Using Polynomial Discrete Time Cellular Neural Networks. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_72
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DOI: https://doi.org/10.1007/978-3-540-72432-2_72
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