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Neural-network climatic parameters structure for refrigerated transportation.

Author(s) : QU R.

Summary

Many problems arise in the designing and controlling process of refrigerated transport because vehicles are viewed as immobile objects and the static climatic parameters are adopted. Considering that traditional mathematic method cannot meet the requirements of dynamic design climatic parameters, this paper analyzes several major parameters which intensely affect the climate, finds and develops the best BP (Back propagation) model by taking advantages of neural network such as the non-linear character and superior study ability, etc. Then, summer's and winter's outdoor dry-temperature as an example is calculated. After experimenting and proofreading work according to national standards, it shows that the error of the output in this model is very small as a whole in spite of some minor flaws in efficiency and partial accuracy. Therefore, the model can simulate the change of the outdoor climatic parameters better and is well worth applying.

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Pages: ICR07-D2-1194

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Details

  • Original title: Neural-network climatic parameters structure for refrigerated transportation.
  • Record ID : 2008-0391
  • Languages: English
  • Source: ICR 2007. Refrigeration Creates the Future. Proceedings of the 22nd IIR International Congress of Refrigeration.
  • Publication date: 2007/08/21

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