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A rigorous design method for binary cellular neural networks

✍ Scribed by Fajfar, Iztok; Bratkovič, Franc; Tuma, Tadej; Puhan, Janez

Publisher
John Wiley and Sons
Year
1998
Tongue
English
Weight
85 KB
Volume
26
Category
Article
ISSN
0098-9886

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✦ Synopsis

In order to be able to take full advantage of the great application potential that lies in cellular neural networks (CNNs) we need to have successful design and learning techniques as well. In almost any analogic CNN algorithm that performs an image processing task, binary CNNs play an important role. We observed that all binary CNNs reported in the literature, except for a connected component detector, exhibit monotonic dynamics. In the paper we show that the local stability of a monotonic binary CNN represents su cient condition for its functionality, i.e. convergence of all initial states to the prescribed global stable equilibria. Based on this ÿnding, we propose a rigorous design method, which results in a set of design constraints in the form of linear inequalities. These are obtained from simple local rules similar to that in elementary cellular automata without having to worry about continuous dynamics of a CNN. In the end we utilize our method to design a new CNN template for detecting holes in a 2D object.

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