Результаты поиска по 'optimization of artificial neural network architecture':
Найдено статей: 2
  1. Shumixin A.G., Boyarshinova A.S.
    Algorithm of artificial neural network architecture and training set size configuration within approximation of dynamic object behavior
    Computer Research and Modeling, 2015, v. 7, no. 2, pp. 243-251

    The article presents an approach to configuration of an artificial neural network architecture and a training set size. Configuration is based on parameter minimization with constraints specifying neural network model quality criteria. The algorithm of artificial neural network architecture and training set size configuration is applied to dynamic object artificial neural network approximation.
    Series of computational experiments were performed. The method is applicable to construction of dynamic object models based on non-linear autocorrelation neural networks.

    Keywords: dynamic object model, training set, artificial neural network, architecture, training, optimization of artificial neural network architecture.
    Views (last year): 2. Citations: 8 (RSCI).
  2. Nikulin A.S., ZHediaevskii D.N., Fedorova E.B.
    Applying artificial neural network for the selection of mixed refrigerant by boiling curve
    Computer Research and Modeling, 2022, v. 14, no. 3, pp. 593-608

    The paper provides a method for selecting the composition of a refrigerant with a given isobaric cooling curve using an artificial neural network (ANN). This method is based on the use of 1D layers of a convolutional neural network. To train the neural network, we applied a technological model of a simple heat exchanger in the UniSim design program, using the Peng – Robinson equation of state.We created synthetic database on isobaric boiling curves of refrigerants of different compositions using the technological model. To record the database, an algorithm was developed in the Python programming language, and information on isobaric boiling curves for 1 049 500 compositions was uploaded using the COM interface. The compositions have generated by Monte Carlo method. Designed architecture of ANN allows select composition of a mixed refrigerant by 101 points of boiling curve. ANN gives mole flows of mixed refrigerant by composition (methane, ethane, propane, nitrogen) on the output layer. For training ANN, we used method of cyclical learning rate. For results demonstration we selected MR composition by natural gas cooling curve with a minimum temperature drop of 3 К and a maximum temperature drop of no more than 10 К, which turn better than we predicted via UniSim SQP optimizer and better than predicted by $k$-nearest neighbors algorithm. A significant value of this article is the fact that an artificial neural network can be used to select the optimal composition of the refrigerant when analyzing the cooling curve of natural gas. This method can help engineers select the composition of the mixed refrigerant in real time, which will help reduce the energy consumption of natural gas liquefaction.

    Keywords: optimization of LNG production, selection of mixed refrigerant composition, big data, neural network, artificial intelligence.

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