| The wood color is one of the important factors deciding consumer impression. In order to improve the wood products'decoration function and product value, and realize the high efficient utilization of the plantation forest, the dyeing technique is needed to improve the plantation wood. An important link in the wood dyeing is color matching. The wood color after dyeing is the key for the product. The artificial color matching requires the personnel of high quality. It is a time-consuming task and is difficult to meet the modern industrial production requirement. In addition, it needs high cost, but it is of poor accuracy. With the increasing need of wood dyeing, after confirming the technique, the computer intelligent color measuring and matching method can be applied into wood dyeing and matching, so as to quicken the dye matching, significantly improve the work efficiency, and save costs.This research took the coniferous tree(Pinus sylvestris) and broadleaf tree(Populus ussuriensis) as the research objects. Based on the research of their dyeing technique, the research discussed that utilizing the traditional computer's color matching model and the intelligent decision making means to deal with the forecast of the wood dyeing color formula. The details included:through test the index related to the dyeing effect and anatomical structure of Pinus sylvestris and Populus ussuriensis, make analysis on the Multiple Regression and Correlation Analysis, to confirm the main anatomical factors affecting the wood dyeing effect. Second, utilize the computer color matching technique's tristimulus values to make research on the matching method of simulated precious wood dyeing formula. Third, utilize the RBF neutral network to establish the formula's forecast model and utilize the simulated precious wood data to verify the model, so as to indicate the effectiveness of the intelligent algorithm applied to forecast the wood dyeing formula. Based on it, an improved RBF neutral network model based on the hidden units will be put forward, and utilize it into the formula forecast. Fourth, according to the determined anatomical features affecting the wood dyeing effect, the input of a forecast model based on the fuzzy neutral network increases the anatomical features; according to the characteristics of the research object, an improved membership grade function is put forward; then, establish the fuzzy neutral network forecast model based on the improved membership grade function. Fifth, establish a wood dyeing formula forecast model based on the motion blur neutral network. At last, realize above methods by C language, and establish the wood dyeing formula forecast platform. It plays important role in the industrial popularity.The research maked following achievements:(1) Analyze the effect of dyeing technique's parameter on the surface color difference and dye absorbing rate; based on the dye concentration, dyeing temperature, dyeing time and bath ratio to analyze the variance of orthogonal test and get the good dyeing technique:dye concentration 1%, penetrating agent's JFC concentration 0.1%, sodium carbonate concentration 2%, NaCl concentration 1.5%, temperature 85℃, dyeing time 60min, fixation time 40 min and bath ratio 17:1.(2) Confirm the main anatomical factors affecting the dyeing effect of Pinus sylvestris: tracheid proportion, wood rays proportion, resin passage proportion, late wood tracheid length; the main anatomical factors affecting the dyeing effect of Populus ussuriensis are:early wood vessel diameter, early wood fiber length, vessel proportion, wood fiber proportion and wood rays proportion.(3) Utilize the computer color matching technique's tristimulus values to calculate the single board dyed simulated previous wood's dye concentration proportion of the Mongolia pine:Simulated Wenge (early wood):reactive brilliant red X-3B is 0.137%; reactive yellow X-R is 0.229%; reactive blue X-R为0.042%. Simulated Wenge (late wood):reactive brilliant redX-3B is 0.176%; reactive yellow X-R is 0.256%; reactive blue X-R is 0.165%. Simulated rose wood (early wood):reactive brilliant red X-3B is 0.117%; reactive yellow X-R is 0.306%; reactive blue X-R is 0.077%. Simulated rose wood (late wood):reactive brilliant red X-3B is 0.162%; reactive yellow X-R is 0.459%; reactive blue X-R is 0.062%; calculate the single board dyed simulated previous wood's dye concentration proportion of the Populus ussuriensis: simulated red sandalwood:reactive brilliant red X-3B is 0.146%; reactive yellow X-R is 0.184%; reactive blue X-R is 0.037%. Simulated cocobolo dalbergia retusa.reactive brilliant red X-3B is 0.361%; reactive yellow X-R is 0.612%; reactive blue X-R is 0.179%. Simulated black walnut:reactive brilliant red X-3B is 0.269%; reactive yellow X-R is 0.203%; reactive blue X-R is 0.074%; simulated teakwood:reactive brilliant red X-3B is 0.122%; reactive yellow X-R is 0.417%; reactive blue X-R is 0.088%。(4) Utilize RBF neutral network to establish the formula forecast model. The result shows that, comparing the two colors spatial simulation effect of Pinus sylvestris, there is a little difference in convergence rate between the L*a*b* space and CMY space. But the average error obtained from the simulation has big difference and is 0.98%and 1.81% respectively. Through the general consideration, it is confirmed to take L*a*b* space as the Pinus sylvestris's research object. Comparing the spatial simulation effect of Populus ussuriensis'two colors, there is a big difference in convergence rate between the L*a*b* space and CMY space,1666 steps and 610 steps respectively. Thus, select CMY space as the spatial object to make research on the color of Populus ussuriensis. Based on the simulated precious wood date, the output data is not very ideal. The maximum error reaches 8.24%.(5) Based on the shortcomings of RBF model, an improved RBF neutral network model based on the hidden units is put forward. The model improvement effectively solves the problems in slow proficiency. The analysis model of Pinus sylvestris can be converged 189 steps and the Populus ussuriensis model can be converged 137 steps. The speed almost reaches the online training standard. Based on the model precision, the Populus ussuriensis model is improved, but not significantly. The Mongolia pine model has no change and sometimes declines. Thus, the model's precision is not improved significantly. Based on the simulated precious wood data, the precision is improved and the maximum error is 4.36%. It shows that the normalization ability of the model needs further improvement.(6) Establish the formula forecast model based on the fuzzy neutral network, put forward an improved membership grade function according to the characteristics of the research object, and establish the formula forecast model based on fuzzy neutral network with the improved membership grade function. The result shows that the error of Mongolia pine and Populus ussuriensis model is improved,0.68%and 0.62%respectively. The result is ideal. Based on the model effect to forecast the simulated precious wood formula, the error has been improved significantly. The maximum error is 1.90%. Basically, it can be accepted.(7) Put forward a forecast model (D-FNN) established based on the motion fuzzy neutral network and put forward a D-FNN model and algorithm suitable for forecasting the wood dyeing formula. The result shows that the model operates fast and the parameter is easily to be set up. There is no need to set up too many fuzzy condition. In addition, the model saves a lot of time. Based on the model effect to forecast the simulated precious wood formula, the error has been improved significantly. The maximum error is 1.25%. It shows that the normalization ability of the model is improved significantly.
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