Research On Forecast Of Wood Properties In Larch Plantation Based On Neural Network

To establish a forecast model of wood properties, the variation regularity of larch plantation was studied by analyzing the properties of larch wood, including the anatomical (tracheid length, tracheid diameter, ratio of tracheid length and width, thickness of tracheid wall, ratio of thecal opening, and microfibril), and physical (width of growth ring, growth rate, late wood rate, and wood density) properties of wood, based on the study on the larch plantation. Moreover, the models of variation regularity and forecast for wood qualities in larch plantation is established.Firstly, the anatomical characters of timber for larch plantation were tested by computer visual analysis system, and physical properties of timber were tested by X-ray density testing system, respectively. The variation regularity of larch plantation was studied, juvenile plantation and matured forest were ploted, and the difference of timber quality for larch juvenile plantation and matured forest were evaluated.Secondly, according to the forecasting methods applied widely nowadays, the most typical characteristic index of wood properties, namely, ratio of length/width of trachied and growth ring density, were selected as analytical data. The feasibility of wood properties forecasting model were established by the methods of linear regression, time series, and neural network and its forecasting precision were compared and proof-tested.. Results indicated that the forecasting model established by neural network method was the best, with smallest error and best precision, and the model was the optimization.Thirdly, the neural network has the advantages of powerful non-linear mapping, self-learning adaptability, simultaneous information processing, modeling for unknown non-linear system. Aimed at the data characters of time series wood properties, the different model network structures, transfer functions and network training functions were determined by the neural network, and the neural network models of different wood properties indexes for larch plantation were established. The precision of the model was verified by data in test group, which indicated that maximal relative error was 4.559%, and minimal was -4.73%. Of all 25 items, the relative errors of 22 items ranged from -2% to 2%, forecasting precision was very high. The forecasting results may entirely meet the demands in practice.In conclusion, the forecast model of timber quality for larch plantation established by neural network method were optimization, with the advantages of small forecasting error, high forecasting precision, and scientific forecasting model. As a result, the model can offer a good theoretical foundation for targeted cultivation of larch plantation.