Bifurcation Processes and Chaotic Phenomena in Cellular Neural Networks
Mario Biey, Marco Gilli, Paolo Checco · PORTO Publications Open Repository TOrino (Politecnico di Torino) · 2001
It is shown that first-order autonomous space-invariant cellular neural networks (CNNs) may exhibit a complex dynamic behavior (i.e. equi- librium point and limit cycle bifurcation, strange and chaotic attractors). The most significant limit cycle bifurcation processes, leading to chaos, are in- vestigated through the computation ofthe corre- sponding Floquet's multipliers and Lyapunov expo- nents. It is worth noting that most practical CNN implementations exploit first order cells and space- invariant templates: so far no example of complex dynamics has been shown in first-order autonomous space-invariant CNNs.