Reby D, Lek S, Dimopoulos I, Joachim J, Lauga J, Aulagnier S
Institut de Recherche sur les Grands Mammifères, I.N.R.A., B.P. 27, 31326 Castanet-Tolosan Cedex France.
CNRS UMR 5576 CESAC, Bat 4R3, Université Paul Sabatier, 118 route de Narbonne, 31062 Toulouse Cedex France.
Behav Processes. 1997 Apr;40(1):35-43. doi: 10.1016/s0376-6357(96)00766-8.
The classification and recognition of individual characteristics and behaviours constitute a preliminary step and is an important objective in the behavioural sciences. Current statistical methods do not always give satisfactory results. To improve performance in this area, we present a methodology based on one of the principles of artificial neural networks: the backpropagation gradient. After summarizing the theoretical construction of the model, we describe how to parameterize a neural network using the example of the individual recognition of vocalizations of four fallow deer (Dama dama). With 100% recognition and 90% prediction success, the results are very promising.
个体特征和行为的分类与识别是行为科学的初步步骤且是一个重要目标。当前的统计方法并不总能给出令人满意的结果。为了提高该领域的性能，我们提出一种基于人工神经网络原理之一的方法：反向传播梯度。在总结模型的理论构建之后，我们以四种黇鹿（Dama dama）发声的个体识别为例描述如何对神经网络进行参数化。识别成功率为100%，预测成功率为90%，结果非常有前景。
