Table 2.

The Chaos Decision Tree Algorithm classified non-biological simulations as stochastic, periodic, or chaotic with high accuracy. These simulated systems include strange non-chaotic attractors (SNAs), linear stochastic processes, and nonlinear stochastic processes, all of which are classically difficult to distinguish from chaos.

	Measurement noise level (% of std. dev.)
System	0%	10%	20%	30%	40%
Cubic map50 (chaotic)	100/100	100/100	100/100	100/100	100/100
Cubic map50 (periodic)	100/100	100/100	100/100	100/100	100/100
Cubic map50 (SNA HH)	100/100	100/100	100/100	100/100	100/100
Cubic map50 (SNA S3)	100/100	100/100	100/100	100/100	0/100
GOPY map51 (SNA)	100/100	100/100	100/100	99/100	14/100
Logistic map52 (chaotic)	100/100	100/100	100/100	100/100	100/100
Logistic map52 (periodic)	100/100	100/100	100/100	100/100	100/100
Lorenz system53 (chaotic)	100/100	100/100	97/100	82/100	36/100
Generalized Hénon map54 (hyperchaotic)	100/100	100/100	100/100	100/100	93/100
Freitas map55 (nonlinear stochastic)	78/100	83/100	98/100	98/100	74/100
Noise-driven sine map55 (nonlinear stochastic)	55/100	3/100	22/100	5/100	78/100
Bounded random walk56 (nonlinear stochastic)	100/100	97/100	59/100	95/100	100/100
Cyclostationary process57 (linear stochastic)	99/100	100/100	99/100	100/100	100/100
ARMA process (linear stochastic)	85/100	98/100	99/100	99/100	100/100
Trended random walk (linear stochastic)	100/100	89/100	90/100	98/100	100/100
Random walk (linear stochastic)	100/100	98/100	100/100	100/100	100/100
Violet noise58 (linear stochastic)	99/100
Blue noise58 (linear stochastic)	100/100
White noise58 (linear stochastic)	100/100
Pink noise58 (linear stochastic)	100/100
Red noise58 (linear stochastic)	100/100