Symbiotic neuron evolution of a neural-network-aided grey model for time series prediction

Shih Hung Yang*, Yon-Ping Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper introduces a symbiotic neuron evolution algorithm (SNEA) to determine the topology of a neural-network-aided grey model (NNAGM) for time series prediction problem. The SNEA uses an evolutionary approach to evolve partially connected neural networks (NNs) and determine the number of hidden neurons. To achieve symbiotic evolution, SNEA first establishes a neuron population where each neuron is randomly created, and evaluates the neurons by constructing NNs with different numbers of neurons. Each neuron shares fitness from participating NNs. This algorithm then performs evolution on the neuron population by crossover and mutation based on neuron fitness. An NNAGM designed by SNEA is applied to the prediction problems and compared with other methods. The experimental results show that SNEA can produce an NNAGM with appropriate topology and higher prediction performance than other methods.

Original languageEnglish
Title of host publicationFUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
Pages195-201
Number of pages7
DOIs
StatePublished - 27 Sep 2011
Event2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan
Duration: 27 Jun 201130 Jun 2011

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
CountryTaiwan
CityTaipei
Period27/06/1130/06/11

Keywords

  • grey model
  • neural network
  • prediction
  • symbiotic evolution

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