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The standard logistic function is the logistic function with parameters =, =, =, which yields = + = + = / / + /.In practice, due to the nature of the exponential function, it is often sufficient to compute the standard logistic function for over a small range of real numbers, such as a range contained in [−6, +6], as it quickly converges very close to its saturation values of 0 and 1.
The Hubbert curve [2] is the first derivative of a logistic function, which has been used for modeling the depletion of crude oil in particular, the depletion of finite mineral resources in general [3] and also population growth patterns. [4] Example of a Hubbert Linearization on the US Lower-48 crude oil production.
The graph on the right is an exponential growth projection made in July 2006. The number of articles on the English Wikipedia up to July 2006 is shown in red, and this is extrapolated in blue using an exponential function (approximately 38000*exp(0.0017 t ) articles, where t is the number of days since January 1, 2001).
It is a symmetric logistic distribution curve, [1] often confused with the "normal" gaussian function. It first appeared in "Nuclear Energy and the Fossil Fuels," geologist M. King Hubbert 's 1956 presentation to the American Petroleum Institute , as an idealized symmetric curve, during his tenure at the Shell Oil Company . [ 1 ]
The generalized logistic function or curve is an extension of the logistic or sigmoid functions. Originally developed for growth modelling, it allows for more flexible S-shaped curves. The function is sometimes named Richards's curve after F. J. Richards, who proposed the general form for the family of models in 1959.
The von Bertalanffy growth function (VBGF), or von Bertalanffy curve, is a type of growth curve for a time series and is named after Ludwig von Bertalanffy. It is a special case of the generalised logistic function. The growth curve is used to model mean length from age in animals. [1]
For the logistic map with r = 4.5, trajectories starting from almost any point in [0, 1] go towards minus infinity. When the parameter r exceeds 4, the vertex r /4 of the logistic map graph exceeds 1. To the extent that the graph penetrates 1, trajectories can escape [0, 1].
One of the most basic and milestone models of population growth was the logistic model of population growth formulated by Pierre François Verhulst in 1838. The logistic model takes the shape of a sigmoid curve and describes the growth of a population as exponential, followed by a decrease in growth, and bound by a carrying capacity due to ...