Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks

  • Karlik B
  • Olgac A
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Abstract

See, stats, and : https : / / www . researchgate . net / publication / 228813985 Performance Functions Neural Article CITATIONS 159 READS 4 , 549 2 , including : Some : Smart Fault Bekir Metropolitan 217 , 422 SEE All . The . Abstract The activation function used to transform the activation level of a unit (neuron) into an output signal . There are a number of common activation functions in use with artificial neural networks (ANN) . The most common choice of activation functions for multi layered perceptron (MLP) is used as transfer functions in research and engineering . Among the reasons for this popularity are its boundedness in the unit interval , the function ' s and its derivative ' s fast computability , and a number of amenable mathematical properties in the realm of approximation theory . However , considering the huge variety of problem domains MLP is applied in , it is intriguing to suspect that specific problems call for single or a set of specific activation functions . The aim of this study is to analyze the performance of generalized MLP architectures which has back - propagation algorithm using various different activation functions for the neurons of hidden and output layers . For experimental comparisons , Bi - polar sigmoid , Uni - polar sigmoid , Tanh , Conic Section , and Radial Bases Function (RBF) were used .

Author-supplied keywords

  • Activation Functions
  • Multi Layered Perceptron
  • Neural Networks
  • Performance Analysis

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Authors

  • Bekir Karlik

  • Ahmet V Olgac

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