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Neural-network-based polynomial correlation of single- and variable-speed compressor performance.

Corrélation polynomiale à base de réseaux neuronaux pour la performance d'un compresseur à vitesse unique ou variable.

Auteurs : ZHAO L. X., ZHANG C. L., GU B.

Type d'article : Article

Résumé

The compressor is one of the major components in a vapour-compression refrigeration system. A neural-network-based polynomial correlation method of positive-displacement compressor performance has been developed that can be applied to both single-speed and variable-speed compressor families. The multi-layer perceptron neural network was used as a universal function approximator. To align with and extend the ARI ten-coefficient correlation method, the third-order polynomial transfer function is customized in the hidden layer and the pure linear function is adopted in the output layer of the neural network. The ARI ten-coefficient correlation has been proven as a special case of the proposed neural network. The new neural network method can be easily extended to multi-input/multi-output cases. In particular, in modelling the performance of a single-speed or variable-speed compressor family, this method gives less than 1% standard deviations and plus or minus 3% maximum deviations against manufacturer data.

Détails

  • Titre original : Neural-network-based polynomial correlation of single- and variable-speed compressor performance.
  • Identifiant de la fiche : 2010-0074
  • Langues : Anglais
  • Source : HVAC&R Research - vol. 15 - n. 2
  • Date d'édition : 03/2009

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