The Modeling Of Wood Drying Schedule Based On Mult-model Data Fusion Modeling Algorithms

Wood is one of the most popular engineering materials. Facing the problems of environmental protection and ecology, which are caused by the decreasing of the world's forest resources, how to use the wood resources effectively,reduce energy consumption and improve the quality of wood products has aroused widespread concern by the governments of many countries. China is a country which is short of forest resources, and timber demand has exceeded supplementation, so in order to make the performance of the timber better, improving timber utilization ratio has become a leading edge research subject in the face of wood scientific workers.Wood drying is an essential link in the wood processing and it is the important technical measure to improve the physical mechanical properties of wood, ensure wood products qualities, and to reduce the loss of wood dropping and to raise wood utilization ratio. Wood drying is a strong coupling,nonlinear and complex dynamic system, and external interference and model uncertainty exists during the drying process. So how to create an effective model of drying is an important foundation study of wood drying, and it is also the pre-condition of realizing full automatic control of drying, improving drying quality, reducing energy consumption and reducing the drying time.In this paper, according the nonlinear characteristics of wood drying process, we set up a wood BP neural network model and a dynamic recursive neural network model respectively, modeling from the static and dynamic aspects of the wood drying process; use self adapting weighted data fusion algorithm and the arithmetic average based on recursive estimation algorithm fused the outputs of the two models, established a fusing model which can be adjusted online according to the different stages of the changes of wood moisture content and improved generation ability of the models.In this paper, in allusion to the strong coupling characteristic of the wood drying process, using multicollinearity analysis of partial least squares and nonlinear regression ability, the regression equation of wood drying process based on the and partial least squares and the mixed model of neural network based on partial least squares are established respectively. The model can response to the more environmental factors which affect the moisture content changes of wood more accurately, and the feasibility and practicality of the methods has been validated through data simulation of a real dry.