What is Sigmoid Curve Function ?

Sigmoid function

A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve.

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 Often, sigmoid function refers to the special case of the logistic function shown in the figure-1 and defined by the formula.

                          Fig 1 : Logistic Curve

                             Fig 2: Error Function

A wide variety of sigmoid functions have been used as the activation function of artificial neurons, including the logistic and hyperbolic tangent functions. Sigmoid curves are also common in statistics as cumulative distribution functions (which go from 0 to 1), such as the integrals of the logistic distribution, the normal distribution, and Student's t probability density functions.

 

Definition

A sigmoid function is a bounded, differentiable, real function that is defined for all real input values and has a non-negative derivative at each point.

Properties

In general, a sigmoid function is real-valued, monotonic, and differentiable having a non-negative first derivative which is bell shaped. A sigmoid function is constrained by a pair of horizontal asymptotes as ${\displaystyle x\rightarrow \pm \infty }$x à +/- ∞

Examples :

Some sigmoid functions compared. In the drawing all functions are normalized in such a way that their slope at the origin is 1

The integral of any continuous, non-negative, "bump-shaped" function will be sigmoidal, thus the cumulative distribution functions for many common probability distributions are sigmoidal. One such example is the error function, which is related to the cumulative distribution function (CDF) of a normal distribution.

Applications

Inverted logistic S-curve to model the relation between wheat yield and soil salinity.

Many natural processes, such as those of complex system learning curves, exhibit a progression from small beginnings that accelerates and approaches a climax over time. When a specific mathematical model is lacking, a sigmoid function is often used.

The van Genuchten-Gupta model is based on an inverted S-curve and applied to the response of crop yield to soil salinity.

Examples of the application of the logistic S-curve to the response of crop yield (barley) to both the soil salinity and depth to watertable in the soil are shown in logistic function#In agriculture: modeling crop response.