Spatial entropy of one-dimensional cellular neural network

dc.citation.epage	2140	en_US
dc.citation.issue	9	en_US
dc.citation.spage	2129	en_US
dc.citation.volume	10	en_US
dc.citation.woscount	8
dc.contributor.author	Lin, SS	en_US
dc.contributor.author	Yang, TS	en_US
dc.contributor.department	應用數學系	zh_TW
dc.contributor.department	Department of Applied Mathematics	en_US
dc.date.accessioned	2014-12-08T15:44:54Z
dc.date.available	2014-12-08T15:44:54Z
dc.date.issued	2000-09-01	en_US
dc.description.abstract	This work investigates the global mosaic pattern and spatial entropy for one-dimensional cellular neural network (CNN). A novel method is developed to partition the parameter space into finitely many regions. The CNNs, with parameters in each region, have the same global pattern. An algorithm is also presented to evaluate the spatial entropy.	en_US
dc.identifier.issn	0218-1274	en_US
dc.identifier.journal	INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS	en_US
dc.identifier.uri	https://ir.lib.nycu.edu.tw/handle/11536/30317
dc.identifier.wosnumber	WOS:000165230500006
dc.language.iso	en_US	en_US
dc.title	Spatial entropy of one-dimensional cellular neural network	en_US
dc.type	Article	en_US

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