A method is proposed for synthesizing cellular neural networks (CNNs) designed for simple applications. Based on the comparison principle for ordinary differential equations, this method leads to a set of inequalities that must be satisfied by the parameters of the cloning template defining the cellular neural network in order to guarantee correct operation for the network. The authors review the architecture of CNNs, compute the bounds of the state and output of a cell, and illustrate how to use this technique to design CNNs for shadowing, motion detection, and hole filling.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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