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Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks
Bekir Karlik, Ahmet Vehbi
Pages - 111 - 122     |    Revised - 31-01-2011     |    Published - 08-02-2011
Volume - 1   Issue - 4    |    Publication Date - December 2010  Table of Contents
Activation Functions, Multi Layered Perceptron, Neural Networks, Performance Analysis
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.
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Dr. Bekir Karlik
- Turkey
Mr. Ahmet Vehbi
Fatih University - Turkey