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Comparison of ReLU and linear saturated activation functions in neural network for universal approximation
2019
Activation functions used in hidden layers directly affect the possibilities for describing nonlinear systems using a feedforward neural network. Furthermore, linear based activation functions are less computationally demanding than their nonlinear alternativ…
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Mathematics

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Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in which they are contained, and quantities and their changes. These topics are represented in modern mathematics with the major subdisciplines of number theory, algebra, geometry, and analysis, respectively. There is no general consensus among mathematicians about a common definition for their academic discipline.

Most mathematical activity involves the discovery of properties of abstract objects and the use of pure reason to prove them. These objects consist of either abstractions from nature or—in modern mathematics—entities that are stipulated to have certain properties, called axioms.

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Comparison of ReLU and linear saturated activation functions in neural network for universal approximation
2019
Activation functions used in hidden layers directly affect the possibilities for describing nonlinear systems using a feedforward neural network. Furthermore, linear based activation functions are less computationally demanding than their nonlinear alternatives. In addition, feedforward neural networks with linear based activation functions can be advantageously used for control of nonlinear systems, as shown in previous authors' publications. This paper aims to compare two types of linear based functions…
Click Mathematics Vs:
Activation Function
Feedforward Neural Network
Rectifier (Neural Networks)
Computer Science
Feed Forward (Control)
Algorithm
Artificial Intelligence
Engineering
Programming Language
Click Mathematics Vs:
Physics
Quantum Mechanics
Control Engineering