A new synthesis procedure for a class of cellular neural networks with space-invariant cloning template
This paper presents a new synthesis procedure (design algorithm) for cellular neural networks (CNN's) with a space-invariant cloning template with applications to associative memories. In the present synthesis procedure, the design problem is formulated as a set of linear inequalities, and the...
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Veröffentlicht in: | IEEE transactions on circuits and systems. 2, Analog and digital signal processing Analog and digital signal processing, 1998-12, Vol.45 (12), p.1601-1605 |
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Sprache: | eng |
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Zusammenfassung: | This paper presents a new synthesis procedure (design algorithm) for cellular neural networks (CNN's) with a space-invariant cloning template with applications to associative memories. In the present synthesis procedure, the design problem is formulated as a set of linear inequalities, and the inequalities are solved using the well-known perceptron training algorithm. Then desired memory patterns are given by a set of bipolar vectors, it is guaranteed that a cellular neural network with a space-invariant cloning template can be designed using the design algorithm developed herein. An algorithm is also provided to design CNN's with space-invariant cloning templates and with symmetric connection matrices to guarantee the global stability of the network. Two specific examples are included to demonstrate the applicability of the methodology developed herein. |
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ISSN: | 1057-7130 1558-125X |
DOI: | 10.1109/82.746682 |