In this brief, it is shown that a two-layer cellular neural network (CNN) with constant templates is suitable to generate self-organizing patterns and, therefore, it is able to model complex phenomena. The dynamic behavior of the single two-layer linear CNN cell is studied and the global behavior of the whole CNN is discussed. Different nonlinear phenomena are generated, including autowaves and spirals. Finally, the sensitivity with respect to parametric uncertainties and noise is investigated.
In this brief, it is shown that a two-layer cellular neural network (CNN) with constant templates is suitable to generate self-organizing patterns and, therefore, it is able to model complex phenomena. The dynamic behavior of the single two-layer linear CNN cell is studied and the global behavior of the whole CNN is discussed. Different nonlinear phenomena are generated, including autowaves and spirals. Finally, the sensitivity with respect to parametric uncertainties and noise is investigated.
Self-organization in a two-layer CNN
ARENA, Paolo Pietro;BAGLIO, Salvatore;FORTUNA, Luigi;
1998-01-01
Abstract
In this brief, it is shown that a two-layer cellular neural network (CNN) with constant templates is suitable to generate self-organizing patterns and, therefore, it is able to model complex phenomena. The dynamic behavior of the single two-layer linear CNN cell is studied and the global behavior of the whole CNN is discussed. Different nonlinear phenomena are generated, including autowaves and spirals. Finally, the sensitivity with respect to parametric uncertainties and noise is investigated.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
