Logistic function
Mathematical function
Nº Q1052379 ★★
Uncommon · Knowledge
Logistic function
Mathematical function
A logistic function or logistic curve is a common S-shaped curve (sigmoid curve) with the equation f ( x ) = L 1 + e − k ( x − x 0 ) {\displaystyle f(x)={\frac {L}{1+e^{-k(x-x_{0})}}}} where L {\displaystyle L} is the carrying capacity, the supremum of the values of the function; k {\displaystyle k} is the logistic growth rate, the steepness of the curve; and x 0 {\displaystyle x_{0}} is the x {\displaystyle x} value of the function's midpoint. The logistic function has domain the real numbers, the limit as x {\displaystyle x} tends to − ∞ {\di...
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From Wikipedia
A logistic function or logistic curve is a common S-shaped curve (sigmoid curve) with the equation f ( x ) = L 1 + e − k ( x − x 0 ) {\displaystyle f(x)={\frac {L}{1+e^{-k(x-x_{0})}}}} where L {\displaystyle L} is the carrying capacity, the supremum of the values of the function; k {\displaystyle k} is the logistic growth rate, the steepness of the curve; and x 0 {\displaystyle x_{0}} is the x {\displaystyle x} value of the function's midpoint. The logistic function has domain the real numbers, the limit as x {\displaystyle x} tends to − ∞ {\displaystyle -\infty } is 0, and the limit as x {\displaystyle x} tends to + ∞ {\displaystyle +\infty } is L {\displaystyle L} . The exponential function with negated argument ( e − x {\displaystyle e^{-x}} ) is used to define the standard logistic function where L = 1 , k = 1 , x 0 = 0 {\displaystyle L=1,k=1,x_{0}=0} , which has the equation f ( x ) = 1 1 + e − x {\displaystyle f(x)={\frac {1}{1+e^{-x}}}} and is sometimes simply called the sigmoid function. It is also sometimes called the expit, being the inverse function of the logit. The logistic function finds applications in a range of fields, including biology (especially ecology), biomathematics, chemistry, demography, economics, geoscience, mathematical psychology, probability, sociology, political science, linguistics, statistics, and artificial neural networks. There are various generalizations, depending on the field.
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