Abstract:

The application of principal component transformation and Bayesian regularization back propagation (BP)neural network in forest volume estimate was introduced through a specific sample in this paper. The difference of forest volume estimate between general back propagation neural network and Bayesian regularization back propagation neural network was compared and the efficiency of estimating forest volume by the means of using original data and transformed data set to establish emulating model was discussed. All the results showed that Bayesian regularization back propagation neural network was more accurate than general BP neural network in estimating forest volume and using transformed data set stemmed from principal component analysis to establish simulating model is more efficient than using original data.

Key words: BP neural network, principal component transformation, generalization, forest volume