关键词: 碳通量, 预测, 自适应, 脊波网络, 稀疏, 超平面奇异特性

Abstract: Carbon flux can be predicted by various ecological factors, because there is a complex and non-linear relationship between them. To increase network training rate and prediction accuracy and according to high dimension, many samples, non-linear, hyperplane singularity characteristics of carbon flux data, a new prediction model based on modified adaptive ridgelet network is proposed. Parameters in activation function are adjusted by Gauss Newton method, and weights and threshold are calculated by matrix partitioned method and pseudo-inverse matrix. Indexes such as convergence rate, number of hidden layer nodes and prediction accuracy of modified adaptive ridgelet network, adaptive ridgelet network and wavelet network are compared. The experimental results show that prediction model proposed in this paper has higher prediction accuracy, sparser network structure and faster training convergence rate.

Key words: carbon flux, prediction, adaptive, ridgelet network, sparse, hyperplane singularity