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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2018

23.06.2017

Evolutionary Based Weight Decaying Method for Neural Network Training

verfasst von: Ioannis G. Tsoulos, Alexandros Tzallas, Dimitris Tsalikakis

Erschienen in: Neural Processing Letters | Ausgabe 2/2018

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Abstract

A new weight decaying technique for neural network training is introduced. The proposed technique utilizes genetic algorithms in conjunction with a local optimization method to restrict the weights of the neural network in some range with desired generalization capabilities. This method is a global optimization one that overcomes most of the problems associated with local optimization procedures. In addition, this technique can be combined with any global optimization procedure from the relevant literature. The proposed technique has been evaluated on several well-known benchmark datasets, along with a series of classification and regression problems. The evaluation results are very promising indicating an improvement in classification error from 25 to 80% for the genetic algorithm.
Vorheriger Artikel Mixed /Passive Projective Synchronization for Nonidentical Uncertain Fractional-Order Neural Networks Based on Adaptive Sliding Mode Control
Nächster Artikel A Hyperplane Clustering Algorithm for Estimating the Mixing Matrix in Sparse Component Analysis

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Metadaten
Titel
Evolutionary Based Weight Decaying Method for Neural Network Training
verfasst von
Ioannis G. Tsoulos
Alexandros Tzallas
Dimitris Tsalikakis
Publikationsdatum
23.06.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9660-0

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