Simulation of properties of composite materials reinforced by carbon nanotubes using perceptron complexes

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Use of algorithms based on neural networks can be inefficient for small amounts of experimental data. Authors consider a solution of this problem in the context of modelling of properties of ceramic composite materials reinforced with carbon nanotubes using perceptron complex. This approach allowed us to obtain a mathematical description of the object of study with a minimal amount of input data (the amount of necessary experimental samples decreased 2–3.3 times). Authors considered different versions of perceptron complex structures. They found that the most appropriate structure has perceptron complex with breakthrough of two input variables. The relative error was only 6%. The selected perceptron complex was shown to be effective for predicting the properties of ceramic composites. The relative errors for output components were 0.3%, 4.2%, 0.4%, 2.9%, and 11.8%.

Keywords: neural network, perceptron complex, mathematical model, simulation, ceramic composite, carbon nanotubes, flexural strength
Citation in English: Dudarov S.P., Diev A.N., Fedosova N.A., Koltsova E.M. Simulation of properties of composite materials reinforced by carbon nanotubes using perceptron complexes // Computer Research and Modeling, 2015, vol. 7, no. 2, pp. 253-262
Citation in English: Dudarov S.P., Diev A.N., Fedosova N.A., Koltsova E.M. Simulation of properties of composite materials reinforced by carbon nanotubes using perceptron complexes // Computer Research and Modeling, 2015, vol. 7, no. 2, pp. 253-262
DOI: 10.20537/2076-7633-2015-7-2-253-262
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