A rigorous design method for binary cellular neural networks

DOI<link itemprop=identifier href="https://doi.org/10.1002/(SICI)1097-007X(199807/08)26:4<365::AID-CTA18>3.0.CO;2-5" /><365::AID-CTA18>3.0.CO;2-5 10.1002/(SICI)1097-007X(199807/08)26:4<365::AID-CTA18>3.0.CO;2-5zbMath0983.94069OpenAlexW2100146730MaRDI QIDQ4226469

Iztok Fajfar, Tadej Tuma, Franc Bratkovič, Janez Puhan

Publication date: 18 May 1999

Full work available at URL: https://doi.org/10.1002/(sici)1097-007x(199807/08)26:4<365::aid-cta18>3.0.co;2-5

zbMATH Keywords

design methodlearning techniquesmonotonic binary cellular neural networks

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Applications of design theory to circuits and networks (94C30)

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Template design methods for binary stable cellular neural networks ⋮ Edge detection of noisy images based on cellular neural networks ⋮ The cloning template design of a cellular neural network. ⋮ Sufficient conditions for one-dimensional cellular neural networks to perform connected component detection

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