人工神经网络建模在抗烧蚀炭/炭复合材料基体改性研究中的应用

基体改件是防止炭／炭复合材料氧化的主要手段。通过将人工神经网络引入炭／炭复合材料的基体改性研究，借助kvenberg-Marquardt算法对不同添加剂组成改性试样所具有的氧化烧蚀率学习，建立了炭／炭复合材料改性添加剂组成一氧化烧蚀率的BP网络模型?研究结果表明：所建模型可以较好地反映添加剂含量与试样氧化烧蚀率间的内在规律，网络模型的输出值和实验验证值间的误差＜0．5％，将模型筛选出的最优配方用于基体改性，试样的氧化烧蚀率下降了49．5％，说明将人工神经网络用于炭／炭复合材料基体改性是可行和有效的。

Application of artificial neural network technique in modifying carbon-carbon composites

A matrix-modification process has great importance for carbon/carbon (C/C) composites. It is the main method to protect C/C composites from oxidation. As is well known, the matrix modification effects are influenced by many complicated factors, so a mathematical model cannot be exactly formulated. In this paper an artificial neural network (ANN) model is developed to predict the burning rate of matrix-modified C/C composites by the use of the Levenberg-Marquardt algorithm. The relationship between the modifying additives and burning rate is analyzed on the basis of the model. Results show that the relative error between the expected value and the predicted output of the network is less than 0.5%. Employing the ANN model, an optimized combination of these additives is obtained. The burning rate of the additive-optimized C/C composite decreases by 49.3%, which indicates that the ANN model is effective and feasible and could be used to reveal the relationships between the additive contents and the burning rate of C/C composites.