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The ramp function is a unary real function, whose graph is shaped like a ramp. It can be expressed by numerous definitions, for example "0 for negative inputs, output equals input for non-negative inputs". The term "ramp" can also be used for other functions obtained by scaling and shifting, and the function in this article is the unit ramp ...
A single sawtooth, or an intermittently triggered sawtooth, is called a ramp waveform. The convention is that a sawtooth wave ramps upward and then sharply drops. In a reverse (or inverse) sawtooth wave, the wave ramps downward and then sharply rises. It can also be considered the extreme case of an asymmetric triangle wave. [2]
The above example simply states that the function takes the value () for all x values larger than a. With this, all the forces acting on a beam can be added, with their respective points of action being the value of a. A particular case is the unit step function,
Therefore, if such a function f is measurable, so is its absolute value | f |, being the sum of two measurable functions. The converse, though, does not necessarily hold: for example, taking f as f = 1 V − 1 2 , {\displaystyle f=1_{V}-{\frac {1}{2}},} where V is a Vitali set , it is clear that f is not measurable, but its absolute value is ...
Plot of the ReLU (blue) and GELU (green) functions near x = 0. In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function [1] [2] is an activation function defined as the non-negative part of its argument, i.e., the ramp function:
if is "squashing", that is, it has limits () < (+), then one can first affinely scale down its x-axis so that its graph looks like a step-function with two sharp "overshoots", then make a linear sum of enough of them to make a "staircase" approximation of the ramp function. With more steps of the staircase, the overshoots smooth out and we get ...
Therefore the "step function" exhibits ramp-like behavior over the domain of [−1, 1], and cannot authentically be a step function, using the half-maximum convention. Unlike the continuous case, the definition of H[0] is significant. The discrete-time unit impulse is the first difference of the discrete-time step
Plot of the softplus function and the ramp function.. In mathematics and machine learning, the softplus function is = (+).It is a smooth approximation (in fact, an analytic function) to the ramp function, which is known as the rectifier or ReLU (rectified linear unit) in machine learning.