Convergence of discrete-time cellular neural networks with interlaced block-sequential dynamics

✍ Scribed by Perfetti, R.

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 223 KB
• Volume: 27
• Category: Article
• ISSN: 0098-9886
• DOI: 10.1002/(sici)1097-007x(199907/08)27:4<367::aid-cta53>3.0.co;2-c

✦ Synopsis

Some interlaced block-sequential modes of operation are introduced for discrete-time cellular neural networks (DTCNN), and the corresponding convergence conditions are investigated. It is proved that DTCNNs, under some block-sequential updating rules, result to be convergent when the feedback templates satisfy some restrictions rather milder than reciprocity or dominance, as required in synchronous mode. Moreover, the set of "xed points of the network results to be independent of the particular updating rule adopted. The drawback of desynchronization is a reduced speed of convergence, which however is tolerable in the usual case when the neighbourhood radius is small.