Title :
On the convergence of reciprocal discrete-time cellular neural networks
Author_Institution :
Inst. di Elettronica, Perugia Univ., Italy
fDate :
4/1/1993 12:00:00 AM
Abstract :
Two results are proved concerning the global convergence of reciprocal discrete-time cellular neural networks (DTCNNs). The first result regards DTCNNs with a piecewise-linear nonlinearity and is an extension of a theorem by N. Fruehauf et al. (1992). The second result regards DTCNNs with threshold-type nonlinearity. Here, convergence is proved under mild conditions assuming a semiparallel operation, that is, only noninteracting cells are updated all at once
Keywords :
discrete time systems; neural nets; piecewise-linear techniques; DTCNNs; convergence; global convergence; noninteracting cells; piecewise-linear nonlinearity; reciprocal discrete-time cellular neural networks; semiparallel operation; threshold-type nonlinearity; Cellular neural networks; Circuits; Cloning; Convergence; Eigenvalues and eigenfunctions; Equations; Piecewise linear techniques; Signal processing;
Journal_Title :
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
