TY - JOUR
T1 - Periodicity as condition to noise robustness for chaotic maps with piecewise constant invariant density
AU - Pareschi, Fabio
AU - Rovatti, Riccardo
AU - Setti, Gianluca
N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15
PY - 2006/1/1
Y1 - 2006/1/1
N2 - Chaotic maps represent an effective method of generating random-like sequences, that combines the benefits of relying on simple, causal models with good unpredictability. However, since chaotic maps behavior is generally strongly dependent on unavoidable implementation errors and external perturbations, the possibility of guaranteeing map statistical robustness is of great practical concern. Here we present a technique to guarantee the independence of the first-order statistics of external perturbations, modeled as an additive, map-independent random variable. The developed criterion applies to a quite general class of maps. © World Scientific Publishing Company.
AB - Chaotic maps represent an effective method of generating random-like sequences, that combines the benefits of relying on simple, causal models with good unpredictability. However, since chaotic maps behavior is generally strongly dependent on unavoidable implementation errors and external perturbations, the possibility of guaranteeing map statistical robustness is of great practical concern. Here we present a technique to guarantee the independence of the first-order statistics of external perturbations, modeled as an additive, map-independent random variable. The developed criterion applies to a quite general class of maps. © World Scientific Publishing Company.
UR - https://www.worldscientific.com/doi/abs/10.1142/S0218127406016872
UR - http://www.scopus.com/inward/record.url?scp=33847611593&partnerID=8YFLogxK
U2 - 10.1142/S0218127406016872
DO - 10.1142/S0218127406016872
M3 - Article
SN - 0218-1274
VL - 16
SP - 3391
EP - 3400
JO - International Journal of Bifurcation and Chaos
JF - International Journal of Bifurcation and Chaos
IS - 11
ER -