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Title:
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Neural networks with periodic and monotonic activation functions: a comparative study in classification problems
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Author:
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Romero Merino, Enrique; Sopena, Josep Maria; Alquézar Mancho, René; Moliner, Joan L.
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
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Abstract:
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This article discusses a number of reasons why the use of
non-monotonic functions as activation functions can lead to a marked
improvement in the performance of a neural network. Using a wide range
of benchmarks we show that a multilayer feed-forward network using
sine activation functions (and an appropriate choice of initial
parameters) learns much faster than one incorporating sigmoid
functions - as much as 150-500 times faster - when both types are
trained with backpropagation. Learning speed also compares favorably
with speeds reported using modified versions of the backpropagation
algorithm. In addition, computational and generalization capacity
increases. |
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Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
-Non-monotonic functions
-Neural networks |
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Rights:
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Document type:
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Article - Published version
Report |
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