标题： 人工神经网络模型的森林树木生长 显示英文标题

标题： Artificial Neural Network Modeling of Forest Tree Growth

摘要： 用人工神经网络（NN）对森林树木生长曲线进行建模的问题被研究。 如果假设每个森林树木样地由几个生长方式不同的子样地组成，则NN的参数形式被证明是一个合适的模型。 在估计NN输出时使用了预测性贝叶斯方法。 使用了相关曲线趋势（CCT）实验的数据。 将NN的预测结果与其中一个最好的参数解法——Schnute模型的预测结果进行了比较。 使用方差分析（ANOVA）方法来评估观察到的差异是否具有统计显著性。 从频率学派的角度来看，Schnute方法和NN方法之间的差异被认为不显著。 然而，贝叶叶ANVO分析表明，NN方法平均产生更好预测的概率为93%。 显示英文摘要

摘要： The problem of modeling forest tree growth curves with an artificial neural network (NN) is examined. The NN parametric form is shown to be a suitable model if each forest tree plot is assumed to consist of several differently growing sub-plots. The predictive Bayesian approach is used in estimating the NN output. Data from the correlated curve trend (CCT) experiments are used. The NN predictions are compared with those of one of the best parametric solutions, the Schnute model. Analysis of variance (ANOVA) methods are used to evaluate whether any observed differences are statistically significant. From a Frequentist perspective the differences between the Schnute and NN approach are found not to be significant. However, a Bayesian ANOVA indicates that there is a 93% probability of the NN approach producing better predictions on average.