Table 4 Parameter settings of SFLA.

From: A novel method based on improved SFLA for IP information extraction from TEM signals

Algorithm

Formula

Parameter setting

Tent-SFLA39

\(x_{n + 1} = \left\{ {\begin{array}{*{20}c} { \, \mu x_{n} , \, x_{n} \in \left( {0,0.5} \right)} \\ {\mu \left( {1 - x_{n} } \right), \, x_{n} \in \left[ {0.5,1} \right]} \\ \end{array} } \right.\)

μ = 2

Logistic-SFLA40

\(x_{n + 1} = \lambda x_{n} \left( {1 - x_{n} } \right){, }\lambda \in [0,4][0,4]\)

λ = 4

Chebyshev-SFLA41

\(x_{n + 1} = \cos \left( {k\cos^{ - 1} \left( {x_{n} } \right)} \right)\)

k = 20

Henon-SFLA42

\(\left\{ {\begin{array}{*{20}c} {x_{n + 1} = y_{n} + 1 - ax_{n}^{2} , a \in \left( {0,1.4} \right]} \\ {y_{n + 1} = bx_{n} , b \in \left( {0.2,0.314} \right]} \\ \end{array} } \right.\)

a = 1.4

b = 0.314

Kent-SFLA43

\(x_{n + 1} = \left\{ {\begin{array}{*{20}c} { \, x_{n} /\gamma , \, x_{n} \in \left( {0,\gamma } \right]} \\ {\left( {1 - x_{n} } \right)/\left( {1 - \gamma } \right), \, x_{n} \in \left( {\gamma ,1} \right]} \\ \end{array} } \right.\)

γ = 0.4