A Generalized Profile Function Model Based on Artificial Intelligence

摘要

A generalized profile function model (GPFM) provides an approximation of individual profile functions of the objects (trees) in a region. It is shown in this paper that this generalized model can be successfully derived using artificial computational intelligence, that is, neural networks. The generalized model (GPFM) is obtained as a mean value of all the available normalized individual profile functions. Generation of GPFM is performed by using the basic dataset, and verification is done by using a validation data set. As an example of the application of the proposed GSPM in volume computing, 42 objects from the same region are considered. Statistical properties of the original, measured data and estimated data based on the generalized model are presented and compared. Testing of the obtained GPFM is performed also by regression analysis. The obtained correlation coefficients between the real data and the estimated data are very high, 0.9946 for the basic data set and 0.9933 for the validation dataset.