Abstract:

Artificial neural network appeals to many ecologists because of the complexity of agroecological systems and its precise predicting ability to simulate vaguely understood and highly uncertain ecosystems. We focus on the introduction of the structure, algorithm and the applications for back-propagation artificial neural network (BPN) in this paper. People usually adopt BPN of a three-layer structure that can approximate functions of any complexity and is not easy to overfit because of its simple structure. The main principle of back-propagation artificial neural network is to adjust weights according to the errors of input neurons. In agroecological research, neural networks are always employed to predict crop yield, biomass yield, relationships between organisms and environmental factors and so on. Previous studies indicate that neural networks greatly outperform linear models, while the accuracy of the results produced by neural networks are very similar to that of algorithmic models. Moreover, neural networks can extrapolate to some degree when enough training data are provided. However, neural networks require a large number of samples to guarantee the robustness of its parameterization, which seems unrealistic for complicated networks to collect such large data sets from crop growth experiments involving countless small plot trials over multiple site-years. The requirement of large amount of training data has hindered the application of neural networks. Some techniques, such as early stopping, jittering and metamodels, have been advanced to induce generalization of neural networks, while techniques such as Garson's algorithm, sensitivity analysis and randomization test are advanced to explain the mechanisms of neural networks. The advantage of neural network lies on its ability to precisely simulate the vaguely understood and uncertain ecosystems, which cannot be realized by traditional approaches. As a nonlinear approximator, artificial neural network is an important tool complementary to comprehensive models.

Key words: Artificial neural networks, Back-propagation, Agro-ecological system, Comprehensive model, Nonlinear model