基于改进的神经网络预测气候因子对大青杨早材r纤维壁厚度和生长速率的影响

摘要

为更好地判断气候因子对木材的材质和生长规律的影响,采用径向基函数(RBF)神经网络模型进行模拟,在此基础上提出了一种自适应RBF神经网络以提高拟合精度.结果表明:基于自适应RBF神经网络建立的早材胞壁率及生长速度影响对气候因子响应模型,可以很好地改进传统RBF算法的不足,此算法能较准确的预测人工林大青杨的生长规律,且相比于传统RBF其仿真速度得到显著提高,误差显著减小.%In order to improve the prediction accuracy of radial basis function ( RBF) neural network model, an adaptive RBF neural network is proposed, based on which establishes the prediction model of climatic factors′effects on early wood′s cell wall ratio and growth rate, which can be very good to improve the lack of traditional RBF algorithm. We can learn from the simulation experiments that the algorithm can predict the growth law of Popu-lus ussuriensis plantation more accurately, at the same time, its simulation speed has been significantly improved, the error has been significantly reduced when compared with the traditional RBF algorithm.