The Application Research Of Improved Genetic Algorithm Of Wood Drying Process

Problems of environmental protection and ecology causing by global forest resources reducing constantly and people's increasing demand for wood constitute a pair of irreconciable contradictions. Therefore, how to use the limited wood resources effectively is critical. Reducing consumption of wood resources and improving the quality of wood products have been aroused extensive attention. How to better the wood performance and raise its utilization ratio has become the focus of the study for wood scientists as our country has fewer forest resources. Wood drying is an important technical measure for improving the wood physical performance, reducing loss of lower grade and raising wood utilization ratio, and also one of key points for guaranteeing the quality of wood products.Drying schedule model describes the relationship between temperature, humidity and wood moisture content. Establishment of this model will realize mathematic model of drying schedule and predict moisture content of drying process, provide effective basis for optimizing drying schedule. Because wood is a porous water seepage substance and hygroscopic material, and the presenting form of moisture in which is various, wood drying process is complex and nonlinear, which makes the establishment of the ideal and realistic drying model become very difficult.This dissertation built wood drying models based on neural network theory and improved genetic algorithm. First, using BP neural network established wood drying schedule model. This paper designed the BP neural network structure and selected the suitable training algorithm, in addition, trained and verified the model through experimental data. The simulation result showed that the model we built is effective, but this model also has some flaws, such as low training speed, weak overall situation search ability, big influence of initial value choice on network astringency and so on.Second, using the improved genetic algorithm optimized the BP neural network wood drying schedule model which had been established. Cloud model is a qualitative and quantitative conversion model proposed by Li Deyi. It has characteristics of uncertainty with certainty, stability with variability in knowledge representation, demonstrates basic principles of species evolution of natural. Improved genetic algorithm based on cloud model is a auto-adapted, high accuracy, fast random searching method combining the excellent characteristic of cloud model with the basic principle of genetic algorithm. This algorithm can avoid local optimal solution and premature convergence problem causing by genetic algorithms, and provide a new method for solving shortcomings in BP network.This paper did simulation study of the model which was optimized by improved genetic algorithm, and its results showed that BP network model solved problems of network discrepancy creating by different initial value and it has better convergence speed and precision comparing to BP network, which proved that genetic algorithm has feasibility and superiority in wood drying.This article proposed an new idea for wood drying system modeling, and provided a number of effective theoretical methods for the establishment of wood drying system.