The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

Abstract

The aim of this paper is to develop neural models enabling the determination of biomechanical parameters for giant miscanthus stems. The static three-point bending test is used to determine the bending strength parameters of the miscanthus stem. In this study, we assume the modulus of elasticity bending and maximum stress in bending as the dependent variables. As independent variables (inputs of the neural network) we assume water content, internode number, maximum bending force value and dimensions characterizing the cross-section of miscanthus stem: maximum and minimum stem diameter and stem wall thickness. The four developed neural models, enabling the determination of the value of the modulus of elasticity in bending and the maximum stress in bending, demonstrate sufficient and even very high accuracy. The neural networks have an average relative error of 2.18%, 2.21%, 3.24% and 0.18% for all data subsets, respectively. The results of the sensitivity analysis confirmed that all input variables are important for the accuracy of the developed neural models—correct semantic models.

Development of technology for accelerated propagation of miscanthus (Miscanthus giganteus)

“…In our country, giant miscanthus in the middle zone of central Russia can produce about 12-15 tons of dry biomass per hectare per year. The interest in this species, in comparison with other species of industrial importance, is also due to such a biological feature as the inability to produce seeds [9]. This is due to the fact that the species Miscanthus giganteus is a hybrid of two other miscanthus species -Miscanthus sinensis and sugarflowered miscanthus.…”