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This paper presents an online learning scheme to train a cellular neural network (CNN) which can be used to model multidimensional systems whose dynamics are governed by partial differential equations (PDE). Most of the existing works in the literature employ fixed parameters which in turn implies an exact knowledge about the underlying PDE and/or its parameters. Moreover, there is a lack of a fast,...
This paper presents an online learning scheme to train a cellular neural network (CNN) which can be used to model multidimensional systems whose dynamics are governed by partial differential equations (PDE). Most of the previous work on CNN, employed fixed parameters or learning methods which need many iterations of an algorithm. There is a lack of fast, online and robust training method in the field...
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