ON THE SPATIO-TEMPORAL DYNAMICS OF A CLASS OF CELLULAR NEURAL NETWORKS
Liviu Goraş, T.D. Teodorescu, Romeo Ghinea · Journal of Circuits Systems and Computers · 2003
The stability and dynamics of a class of Cellular Neural Networks (CNNs) in the central linear part is investigated using the decoupling technique based on discrete spatial transforms, Nyquist and root locus techniques. The influence of the cell order and template neighborhood is discussed and computer simulations are presented. It is shown that, as in the case of Turing patterns, for 1D CNNs, the patterns predicted by the linear theory of the decoupling technique are often valid even when the nonlinearity has been reached.